Entry Level Qualification 

Post Graduate

Career Fields 

Information Technology & Computer Science, Mathematics & Science

For Specially Abled 

Career Entrance Exam 

NIMCET MCA

About Career 

As a Computational Scientist, you will – (i) build computational models and simulation models and (ii) develop and write algorithms using advanced mathematical and computational techniques that can be applied to solve complex scientific, technological, and engineering problems.

Computational Scientists are called in when studying a natural system (physical, chemical, biological, environmental, etc.) or solving a complex scientific problem (such as out of 2,000 probable genes which one is responsible for a certain disease) require dealing with massive amount of data throughput – or putting it simply, a massive amount of data inputs and analysis. This kind of data analysis cannot be done by simple computing that we do using data analytic software on our computers but requires high-performance computing (HPC) or high-throughput computing (HTC).  

This is high-end science and unless you are a computer geek, you might have already found it slightly difficult to understand what is being written.

Firstly, understand a complex natural system that Computational Scientists deal with

Take, for example, daily weather forecasting. This involves analysis of a humongous amount of data obtained from satellite images, remote sensing data, and data from various other weather observation tools. Understand that these data flow is continuous, and, in every minute, thousands of Gigabytes of data needs to be analyzed. Weather depends on a very large number of factors – data on which need to be analyzed to forecast weather.

So, this is a complex system and forecasting is a complex task. Computational Scientists pitch in by using complex mathematical techniques to build computational models and write algorithms, which, when run on high-performance computing systems (which can carry out a massive amount of computations or calculation simultaneously), can accurately forecast daily weather.

What is a computational model? And what is an algorithm?

A computational model comprises one or more mathematical formulas and functions to enumerate or describe how, given a set of input data, output information will be computed (or calculated). Remember the basic algebraic formula, (a+b)² = a² + 2ab + b²?Well, computational models will have similar but much more complex formula for computation of outputs from a given set of input data.

An algorithm is a sequential set of instructions that could be executed by a computer. An algorithm is a set of well-defined specifications, arranged sequentially, that can be used to write a computer program for performing a task, calculation, data processing, data analysis, and so on.

Secondly, understand a complex problem that Computational Scientists deal with

Take, for example, identifying the gene responsible for a specific disease. This task may require sequencing 2,000 probable genes and finding the exact sequence or gene which might be responsible for producing a protein that in turn may trigger a disease.

Sequencing genes means laying out the sequence of the nitrogenous bases (Adenine, Cytosine, Guanine, and Thymine – also called nucleotide bases) in the strands of DNA, right? Basic Biology. DNA has a double helix structure made up of two spiral chains or strands of deoxyribonucleic acid. These two strands are held together by the nucleotide bases bonded in pairs (Adenine or A bonds with Thymine or T and Guanine or G bonds with Cytosine of C. The outer sides of the strands are made up of deoxyribose sugar and phosphate.

Now, in each of the strands, the nucleotide bases appear in a sequence. A gene is a specific sequence of the bases which can produce a protein. If the sequence of the bases is laid out on paper, it may look like – GATTGTACATGT and so on. It could be a very long sequence.  Now, multiply this with 2,000 for the example task in hand.

For processing such mega volume of data, Computational Scientists are called in. They build computational models and write algorithms which can carry out the sequencing tasks in the fastest possible time using high-performance computing.

Getting the idea?

An idea of the volume of high-throughput data that Computational Scientists might be dealing with

Let us stick to the gene sequencing task for giving you an example. To give you a basic idea, around 2, 50,000 human gene sequence data that are available to the scientist now could be equal to about 25 petabytes (YouTube generates about 100 petabytes of data annually now).

1 petabyte = 1000 TB or terabyte

1 TB or terabyte = 1000 Gigabyte

Scientists are predicting that within the next decade, the amount of human gene sequence data will be approximately between 10-40 exabytes a year.

1 exabyte = 1000 petabyte

Now you can imagine that processing this large volume of data or even a fraction of this large volume of data requires complex computational models and high-end computer processing power.

So, what will you do as a Computational Scientist?

First,

You will study and understand the system or environment or the conceptual framework to which a complex task or complex problem belongs to. For example, climate conditions and weather conditions in case of the weather forecasting problem. Molecular Biology and Genetics for taking on the gene sequencing task. Or understanding the Physics or Chemistry or the technology behind a scientific or technical problem.

Second,

Understand the task and the problems, or you will frame or conceptualize the task and problem yourself.

Third,

Develop a computational model; or in some cases, a simulation model using complex mathematics and computer science concepts.

Simulation means approximate imitation of a real-life system or process – simulation is nowadays used when the real system is too complex and takes a lot of time to observe to get data or when the real system could be too dangerous to engage or when a system is being built and many forms of the systems need to be tested – for example, chemical analysis of a very large number of compounds to identify a molecule which may treat a drug; another example – simulation of car crashes to understand what safety features may be useful; another example – simulation of living conditions in Mars to design the right kind of bodysuit).

Fourth,

Write the required algorithms for computers to execute the computational or simulation models.

Fifth,

Decide upon the right computing processing power (such as high-performance computing, high throughput computing, distributed and parallel computing, etc.). Heard of Super Computers, right? Super Computers have high-performance computing powers. Distributed and parallel computing engages a very large number of processors simultaneously.

Sixth,

Analyze the outputs from the computations and validate the models.

Remember that

Computational Scientists are not Computer Scientists or Computer Engineers. A Computer Scientist/ Computer Engineer may find work in Computational Science. However, with a good number of years of experience in Computational Science, a Computer Scientist/ Computer Engineer may call herself a Computational Scientist.

The fundamental difference is that a Computer Scientist and Engineer are involved in designing, developing, installation, testing, and maintenance of computer hardware and software. They may sometime use basic computational techniques in software development.

But then, they will not be able to do the high computational modeling and algorithm development that a Computational Scientist can do.

So, Computational Scientists are not involved in hardware development and software development. They may do programming a lot, but their primary purpose is to build computational models, simulation models, and algorithms for solving complex scientific, engineering, and other problems.

Key Roles and Responsibilities

As a Computational Scientist you will be responsible for one or more of the following roles or associated tasks:

1. You will analyze and interpret high-throughput data. High-throughput technology means automation of experiments such that large scale repetition becomes feasible i.e. any technology/instrument that generates large data-sets with more than 10000 data points by performing repetitive tasks and enables you to work on it directly.

2. You will study and understanding relevant datasets, both those generated internally in your organization and those from the public domain.

3. You will select, design & oversee appropriate in vitro(synthetic / chemical medium) and in vivo(biological medium) studies to support model development and validate computational predictions.

4. You will develop and apply novel computational techniques for interpretation of high-throughput biological/ physical/ chemical/ technological/ engineering data.

5. You will develop algorithms and computational methodologies applicable to high-performance computation.

6. You will contribute subject matter expertise to software development teams for supporting the creation of machine learning algorithms & advanced analytical methodologies required to provide high-confidence predictive information.

You may have to use your programming skills as well using popular scripting languages such as Fortran, C/C++, Python, JAVA / Scala, Mathematica, R with analytical or scientific software relevant to your industry such as SAS, BioPerl, ClustalW, ENSEMBL, GenBank, GenePattern, Illumina LIMS, SOLiD, Vector NTI, NCBI RefSeq, ChemStation, Minitab, CALACO, Chem 4-D, Benfield Re Metrical, Sigma Stat etc. and 1 or more machine learning libraries such as sci-kit-learn, MLlib, Tensor Flow, PyTorch, Keras, Caffe or Theano etc.

7. You will employ computational tools to run analyses for various compound sets, compile and interpret results, and develop summary reports.

8. You will develop technical documentation that includes methods, procedures and analytical data including interpretation of results and a thorough impact analysis.

9. You will be preparing reports for communicating data analysis and modeling results to other scientists, technologists, and engineers.

Career Entry Pathway 

Class 10 all subjects as per scheme of studies – Class 11-12 Physics, Chemistry, and Mathematics along with any other subject as per scheme of studies – BE/ B. Tech/ similar degree in Computer Science & Engineering / Electrical Engineering/ Electronics Engineering/ Data Science & Engineering/ Similar – ME/M.Tech. in Computer Science / Computational Science/ Data Science & Engineering/ similar

After completing Class 11-12 Physics, Chemistry, Mathematics along with any other subject as per scheme of studies, you can do a Bachelor’s degree in Computer Science & Engineering/ Electrical Engineering/ Electronics Engineering/ Electrical & Electronics Engineering/ Electronics & Communication Engineering / Data Science & Engineering or a in a similar field. Then you can do an ME/ M.Tech. in Computer Science/ Computer Science & Engineering/ Computational Science/ Data Science & Engineering / in a similar field to get a work opportunity in Computational Science.

Class 10 all subjects as per scheme of studies – Class 11-12 Physics, Chemistry, and Mathematics along with any other subject as per scheme of studies – BE/ B. Tech/ similar degree in Computer Science & Engineering / Electrical Engineering/ Electronics Engineering/ Data Science & Engineering/ Similar – ME/M.Tech. in Computer Science / Computational Science/ Data Science & Engineering/similar – Ph.D. in Computational Science/ Data Science & Engineering/ similar

After completing Class 11-12 Physics, Chemistry, Mathematics along with any other subject as per scheme of studies, you can do a Bachelor’s degree in Computer Science & Engineering/ Electrical Engineering/ Electronics Engineering/ Electrical & Electronics Engineering/ Electronics & Communication Engineering / Data Science & Engineering/or a in a similar field. Then you can do an ME/ M.Tech. in Computer Science/ Computer Science & Engineering/ Computational Science/ Data Science & Engineering/in a similar field. You can then go for a Ph.D. in Computational Science/ Data Science & Engineering/ in a similar field to get a very good work opportunity in Computational Science.

Class 10 all subjects as per scheme of studies – Class 11-12 Mathematics along with any other subject as per scheme of studies – Bachelor’s degree in Statistics/ Mathematics / Computer Science/ Data Science/ Similar field – Master’s degree in Statistics/Mathematics/ Data Science – Ph.D. in Computational Science/ Statistics/ Mathematics/ Data Science/ similar field.

After completing Class 11-12 Mathematics along with any other subject, you can study for a B. Stat / B. Math or for a B.Sc./B.S. in Statistics/ Mathematics/ Applied Statistics/ Applied Mathematics/ Computer Science/ Data Science/or in a similar field. Then you can do a Master’s degree in Statistics/ Mathematics/ Applied Statistics/ Applied Mathematics/ Computer Science/ Computational Science/ Data Science/or in a similar field. To get a very good opportunity, you can then do a Ph.D. in Computation Science/ Statistics/ Mathematics/ Computational Mathematics/ Data Science/in a similar field.

Class 10 all subjects as per scheme of studies – Class 11-12 Physics, Chemistry, Mathematics along with any other subject as per scheme of studies – Bachelor degree in Engineering – Post graduation or Certificate course / PG Dip / Advanced Certificate course - Internships / summer projects / lab or course works

After completing Class 11-12 Physics, Chemistry, Mathematics with or without Computer along with any other subject as per scheme of studies, you can go for a BCA or Bachelor degree Engineering like Computer Science / Telecommunication / Bioengineering / Electrical & Electronics / Aeronautical & Astronautical Engineering Energy or Power Engineering or similar. Then follow it up with post-graduation in the respective field or in Computer Science, Computer Science & Engineering, Bioengineering, Electrical & Electronics Engineering, Electronics Engineering, Materials Science or any other Engineering discipline or IT. Thereafter, in order to build a stronger foundation, go for Certificate course / PG Dip / Advanced Certificate course in NLP (Natural Language Processing)/ Python / R / Java / C++ or similar technologies relevant to your domain of study. Then you must complete internships / summer projects / lab or course works in computational fields relevant to your domain of study if you do not want to go for a PhD. However, in this field most professionals hold a Doctoral degree.

Class 10 all subjects as per scheme of studies – Class 11-12 Physics, Chemistry, and Biology with or without Mathematics along with any other subject as per scheme of studies - Bachelor degree program in any discipline of Life Sciences - PG - internships / summer projects / lab or course works in computational fields

After completing Class 11-12 Physics, Chemistry, Biology with or without Mathematics along with any other subject as per scheme of studies, you can go for a Bachelor degree program in any discipline of Life Sciences. Then go for post-graduation in your respective field or in Biotechnology, Biophysics, Bioinformatics, Systems Biology, Biostatistics, Immunology, Genetics & Genomics or similar and follow it up with internships / summer projects / lab or course works in computational fields relevant to your domain of study if you do not want to go for a PhD. However, in this field most professionals hold a Doctoral degree. You will need proven exposure to one or more analytical / scientific software such as BioPerl, The MathWorks MATLAB, ClustalW, ENSEMBL, GenBank, GenePattern, Illumina Laboratory Information Management System LIMS, Life Technologies SOLiD, Life Technologies Vector NTI, NCBI RefSeq etc.

Class 10 all subjects as per scheme of studies – Class 11-12 Physics, Chemistry, and Mathematics along with any other subject as per scheme of studies – Bachelor degree in Mathematics /Statistics / Physics / Chemistry – Post graduation - Certificate course / PG Dip / Advanced Certificate

After completing Class 11-12 Physics, Chemistry, Mathematics along with any other subject as per scheme of studies, go for a Bachelor degree in Mathematics or Statistics or Physics or Chemistry. Then go for post-graduation in Applied Mathematics / Applied Statistics / Applied Physics / Applied or Analytics chemistry / Atmospheric Sciences, Meteorological Sciences, Geology / Geophysics, Climate Sciences, Materials Science or similar. You may follow it up with Certificate course / PG Dip / Advanced Certificate course in NLP (Natural Language Processing)/ Python / R / Java / C++, Machine Learning, Data Mining, Mathematical & Statistical Modeling, Mathematical Optimization & Simulations or similar technologies relevant to your domain of study.

Class 10 all subjects as per scheme of studies – Class 11-12 Physics, Chemistry, and Mathematics along with any other subject as per scheme of studies - Bachelor degree in Mathematics or Statistics or Physics - PG - Additional course (Certificate course or PG Dip or Advanced Certificate course)

After completing Class 11-12 Physics, Chemistry, Mathematics & any other subjects as per scheme of studies, you can go for a Bachelor degree in Mathematics or Statistics or Physics. Then complete post-graduation in your respective field. Follow it up with Additional course (Certificate course or PG Dip or Advanced Certificate course) on R or Mathematical and Statistical Modeling or Mathematical Optimization or Simulations or Modeling& Simulation Engineering or Fuzzy Optimization Techniques or Boundary Layer Theory or Bio-Mathematics or similar.

Class 10 all subjects as per scheme of studies – Class 11-12 Physics, Chemistry, Mathematics along with any other subject as per scheme of studies – B Tech/ BCA – Post graduation – PhD

After completing Class 11-12 Physics, Chemistry, Mathematics with or without Computer along with any other subject as per scheme of studies, you can go for a BCA or Bachelor degree Engineering like Computer Science / Telecommunication / Electrical & Electronics / Aeronautical & Astronautical Engineering/ Energy or Power Engineering or similar. Then follow it up with post-graduation in your respective field or in Computer Science, Computer Science & Engineering, Electrical & Electronics Engineering, Electronics Engineering, Materials Science or any other Engineering discipline or IT. Then go for a PhD in Engineering, Computational Design, Power Systems, Computer Science, Materials Sc or related areas.

Class 10 all subjects as per scheme of studies – Class 11-12 Physics, Chemistry, Mathematics along with any other subject as per scheme of studies – Bachelor degree in Mathematics /Statistics / Physics/ Chemistry – Post graduation in Applied Mathematics or Applied Statistics or Applied Physics or Applied or Analytics chemistry or Atmospheric Sciences, Materials Sc, Geology or Geophysics, Climate Sciences or Meteorological Sciences – PhD

After completing Class 11-12 Physics, Chemistry, and Mathematics along with any other subject as per scheme of studies, go for a Bachelor degree in Mathematics or Statistics or Physics or Chemistry. Then go for post-graduation in Applied Mathematics / Applied Statistics / Applied Physics / Applied or Analytics chemistry / Atmospheric Sciences, Meteorological Sciences, Geology / Geophysics, Climate Sciences, Materials Science or similar. Then complete your Doctoral studies in Particle Physics, fluid dynamics, Solid State Physics, atomic sciences, Applied Physics, Quantum Physics, Atmospheric Sciences, Geology or Geophysics, Climate Sciences, Materials Sc, protein or small ligand modeling, computational mathematics, computational statistics, analytics or applied chemistry or similar

Class 10 all subjects as per scheme of studies – Class 11-12 Physics, Chemistry, Biology with or without Mathematics along with any other subject as per scheme of studies – Bachelor degree program in any discipline of Life Sciences - PG - PhD

After completing Class 11-12 Physics, Chemistry, Biology with or without Mathematics along with any other subject as per scheme of studies, you can go for a Bachelor degree program in any discipline of Life Sciences. Then go for post-graduation in your respective field or in Biotechnology, Biophysics, Bioinformatics, Systems Biology, Biostatistics, Immunology, and Genetics & Genomics or similar. Then go for your Doctoral studies in molecular modeling, immunotherapy, cancer biology, genomics, computational pathology, biophysics, bioinformatics, systems biology, transcriptomics, gene networks, metabolomics etc.

Class 10 all subjects as per scheme of studies – Class 11-12 Physics, Chemistry, Mathematics along with any other subject as per scheme of studies – Bachelor degree in Atmospheric Sciences, Meteorological Sciences, Geology or Geophysics, Climate Sciences or similar – Post graduation in Atmospheric Sciences, Geology or Geophysics, Climate Sciences or similar – M.Phil. or PhD in atmospheric modeling, geology or geophysics, geodynamics, climate sciences or similar

​​​​​​​

After completing Class 11-12 Physics, Chemistry, Mathematics along with any other subject as per scheme of studies, go for a Bachelor degree in Atmospheric Sciences, Meteorological Sciences, Geology or Geophysics, Climate Sciences or similar. Then go for post-graduation in your respective field followed by MPhil / PhD in various topics pertinent to your domain of study. Some examples could be Condensation-Induced Atmospheric Dynamics, Biomass Burning Aerosols, Atmospheric Weather Variability, Heat Uptake and Release over Oceans, tropical cyclones, Orogenic Cycles, High-Pressure Northwestern Cyclades, Analog Modelling for Testing Kinematic Boundary Conditions, Granulite Metamorphism etc.

Required Qualification & Competencies 

To become a Computational Scientist, you must complete class 10 with subjects as per scheme of studies and then study class 11-12 with these combinations as per scheme of studies:

1. With Mathematics as a subject

2. With Physics, Chemistry, Mathematics, Computer Science/Biology/Statistics

3. With Physics, Chemistry, Mathematics

4. With Physics, Chemistry, Biology, Mathematics/Computer Science/Statistics (Mathematics is suggested)

5. With Physics, Chemistry, Mathematics

Then you can do a degree in Engineering/ Technology/ Science/ Mathematical Science in any one of the following fields. A post-graduation degree is preferred, and a Ph.D. is the best option to get a very good job in this field.

You can study for a Bachelor’s or Master’s or Doctoral degree in any of the following fields (Note that all these fields may not offer you a degree at all 3 levels that is in Bachelor’s, Master’s and Doctoral. Some fields may offer a degree only at the Master’s or at the Doctoral level):

1. Applied Mathematics

2. Applied Statistics

3. Artificial Intelligence & Machine Learning

4. Atmospheric Sciences

5. Bioengineering

6. Bioinformatics

7. Biosciences

8. Biostatistics

9. Chemistry

10. Computational Mathematics

11. Computational Natural Sciences

12. Computational Physical Sciences

13. Computational Physics

14. Computational Sciences

15. Computational Sciences & Engineering

16. Computational Social Science

17. Computer Applications

18. Computer Science

19. Computer Science & Engineering

20. Data Science & Engineering

21. Data Sciences

22. Distributed & Parallel Computing

23. Electrical and Computer Engineering

24. Electrical and Electronics Engineering

25. Electrical Engineering

26. Electronics & Communication Engineering

27. Electronics and Computer Engineering

28. Electronics Engineering

29. Financial Mathematics

30. Geoinformatics

31. Geomatics and GIS

32. Life Science

33. Machine Learning

34. Mathematical Biology

35. Mathematical Physics

36. Mathematical Statistics

37. Mathematics

38. Mathematics and Computer Science

39. Mathematics and Statistics

40. Molecular & Cellular Engineering

41. Physics

42. Quantitative Economics

43. Quantum Computing

44. Statistics and Computer Science

Compentencies Required

Interests

1. You should have interests for Investigative Occupations. Investigative occupations involve working with ideas and quite a lot of thinking, often abstract or conceptual thinking. These involve learning about facts and figures; involve the use of data analysis, assessment of situations, decision making and problem-solving.

2. You should have interests for Enterprising Occupations. Enterprising occupations involve taking initiatives, initiating actions, and planning to achieve goals, often business goals. These involve gathering resources and leading people to get things done. These require decision making, risk-taking, and action orientation.

3. You should have interests for Realistic Occupations. Realistic occupations often involve physical activities for getting things done using various tools and equipment.

Knowledge

1. You should have knowledge of Computers– Knowledge of computer hardware and software, computer programming, computer networks, computer, and mobile applications.

2. You should have knowledge of analytical or  scientific software relevant to your industry such as SAS, BioPerl, ClustalW, ENSEMBL, GenBank, GenePattern, Illumina Laboratory Information Management System LIMS, Life Technologies SOLiD, Life Technologies Vector NTI, NCBI RefSeq, Agilent ChemStation, Minitab, Vogel Scientific Software Group CALACO, ChemInnovation Software Chem 4-D, Benfield ReMetrica, Systat Software Sigma Stat etc.

3. You should have experience in the broad application of 1 or more higher-level programming languages such as Python, Java/Scala, Mat lab, R or C/C++.

4. You may need experience with one or more machine learning libraries such as sci-kit-learn, MLlib, TensorFlow, PyTorch, Keras, Caffe or Theano, etc. and at least one data-analysis or scripting language (e.g. The MathWorks MATLAB, Mathematica, Python, R)

Skills

1. You should have Scientific Skills - in using various scientific rules and methods to get things done or solve problems.

2. You should have Technical Skills - using various technologies and technical methods to get things done or solve problems.

3. You should have Quality Control Analysis Skills - conducting tests and inspections of products, services, or processes to evaluate quality or performance.

4. You should have experience working independently under general direction within the scope of an assignment and use sound judgment in determining methods, techniques, and evaluation criteria.

5. You should have Systems Analysis Skills - determining how a system should work and how changes in conditions, operations, or the environment will affect outcomes.

6. You should have enough verbal and written communication skills necessary to effectively collaborate in a team environment and present technical ideas/results.

7. You should have Critical Thinking skills- Skills in the analysis of complex situations, using logic and reasoning to understand the situations and take appropriate actions or make interpretations and inferences.

8. You should have Judgment and Decision Making Skills - considering pros and cons of various decision alternatives; considering costs and benefits; taking appropriate and suitable decisions.

9. You should have Problem Solving Skills - Skills in analysis and understanding of problems, evaluating various options to solve the problems and using the best option to solve the problems.

10. You may need Programming Skills - writing computer programs for various applications, installation of computer programs and troubleshooting of problems in computer programs or software.

11. You should have Systems Evaluation Skills - identifying measures or indicators of system performance and the actions needed to improve or correct performance, relative to the goals of the system.

Ability

1. You should have Deductive Reasoning Ability - apply general rules and common logic to specific problems to produce answers that are logical and make sense. For example, understanding the reasons behind an event or a situation using general rules and common logic.

2. You should have Problem Sensitivity - The ability to tell when something is wrong or is likely to go wrong. It does not involve solving the problem, only recognizing there is a problem.

3. You should have Inductive Reasoning Ability - to combine pieces of information from various sources, concepts, and theories to form general rules or conclusions. For example, analyzing various events or situations to come out with a set of rules or conclusions.

4. You should have Information Ordering Ability - to arrange things or actions in a certain order or pattern according to a specific rule or set of rules (e.g., patterns of numbers, letters, words, pictures, mathematical operations).

5. You should have Oral Comprehension Ability - listen to and understand information and ideas presented through spoken words and sentences.

6. You should have Oral Expression Ability - communicate information and ideas in speaking so others will understand.

7. You should have Fluency of Ideas - The ability to come up with several ideas about a topic (the number of ideas is important, not their quality, correctness, or creativity).

Personality Traits

1. You are always imaginative or in most situations.

2. You are always or mostly care about your actions and behavior.

3. You are always or mostly disciplined in your action and behavior.

4. You are always calm or generally remain calm in most situations.

5. You can always act independently or could do so in most situations.

6. You always prefer to experience new things and have new experiences, or you mostly do.

Career - Job Opportunities & Profiles 

Following are some of the entry-level roles that you may find after at least your post-graduation. In India, such roles after Bachelor degrees is few and far between unless it is coupled to a few professional certificates. Some recruiters are nowadays looking for knowledge & experience instead of degrees so you may as well obtain informal training after your Bachelor degree to elevate your knowledge base and widen foundation and smoothen your efforts while scouting for jobs. But overall, a Master’s degree and a Ph.D. are the best possible combination to get a job in this field.

Some of the roles in which you may get an opportunity are:

1. Computational Scientist

2. Data Scientist

3. Associate Computational Scientist

4. Computational Geneticist

5. Computational Statistician

6. Computational Scientific Programmer

7. Applied Mathematician

8. Applied Statistician

9. Computational Mathematician

10. Decision Scientist

11. Computational Scientist, Translational Sciences

12. Computational Scientist (Software Programming)

13. Computational Chemistry Scientist

14. Computational Scientist (Mammalian Bioinformatics)

15. Computational Biologist

16. Computational Scientist - Earth System Analysis, Atmospheric Modeling, Geosciences

17. Computational Physicist

18. Computational Fluid Scientist

19. Computational Scientist - Immunobiology or Immunotherapy or Antibody Engineering

20. Computational Toxicology – Scientist

1. You may work with companies or enterprises or  organizations operating in the Life Sciences or  Therapeutics or  Healthcare domain or Biotechnology or  Pharmaceuticals or diagnostics domains within the R&D sections or  Translational Sciences section (bridging the research findings with the clinical programs) participating in discovery, development, implementation or for analysis – decision making, etc. There are various roles in such companies, and they are all laboratory-based (dry lab orinsilico facilities). These will include genomics or gene therapy or immunotherapies or metabolomics or computational pathology or transcriptomics or toxicology etc.

2. You may work with companies operating in the data analytics or IT domain to develop algorithms and analytic approaches towards a scientific problem in the capacity of a Computational Scientific Programmer or similar roles. You may have to lead or participate in computer programming efforts in a variety of clinical projects or atomic or nuclear projects or maybe power or coal engineering projects or say projects targeting national defense-security objectives. These are some examples; the scope or applicability of programming projects may be very different and widespread.

3. You may work with laboratories (government-owned or privately held) which are innovating into the development of new materials through high-fidelity physical or mathematical or statistical models at multiple scales combined into a single multi-scale or multi-physics model using numerical methods and associated algorithms. Such opportunities include defense research organizations, operations research or studies involving polycrystalline metals and ceramics from atomistic to mesoscale as well. Computational methods are utilized for nanocrystalline materials, interfacial design, high strength steels, lightweight materials, etc.

4. You may work with laboratories (government-owned or privately held) which are innovating into natural hazards research or simply studying the varied dynamics of the earth or the atmosphere or climate through simulation modeling and analysis. Such analyses may go into policy and law-making too.

5. You may work with leading property insurance providers concerned with state-of-the-art loss-prevention engineering and research that covers response and damage of structural & non-structural systems, contents or equipment in the capacity of research scientist (structural mechanics) or similar roles. This will also include studying severe weather, climate change, and other atmospheric perils, and the impacts on building components within the meteorological sciences divisions. Such opportunities also include studying the hazards and effects of earthquakes and tsunamis.

6. As a Computational Scientist working in the area of Atmospheric or related Sciences, there may be several other opportunities too with laboratories or research organizations pursuing studies in the domains like aerosols & clouds, meteorology, radiation, stratospheric physics or similar areas.

Specialisation Tracks In This Career

Computational Statistician

Computational Statisticians utilize statistical modeling, large scale parameter estimation, and advanced data analysis to propose innovative ways to look at solutions to problems by using statistical experiments/ test hypotheses, and they may build machine-learning models too. All these methods help businesses to validate important decisions, better understand their customers, increase conversions and growth. They work at supercomputing facilities to represent and analyze the largest of datasets.

Computational Geneticist

Computational Geneticists identify the molecular circuits underpinning complex human genes. They may use mapping techniques to correlate human genetic information with that in other primates or even non-primates. They construct multiscale gene regulatory network models or maybe the gene network(s) that have become dysfunctional within an individual patient (if they are working in the healthcare industry). Most geneticists find opportunities within the healthcare domain as that is the dominant sector in the industry today. Their work may revolve around genomics, gene therapy or even epigenetics (modifications of the outer shell of genetic material without affecting the inner constitutional molecules).

Decision Scientist

Decision Scientists need experience in a mathematical, engineering, or scientific field. A combination of knowledge in computational areas such as data science, computer science, machine learning, AI, statistics, or graph algorithmsis required to succeed in this field to create new data-based capabilities; let’s say for an example handling online interactions or multichannel online purchases a day and more, analyzing the traffic, understanding business opportunities, drawing graph-based inferences, computing solutions to common recurring problems, and deciding what’s good for the business. They are typically CSc, Math or Stat postgraduates.

Scientist - Computational Chemistry

Computational Chemistry Scientists are trained in molecular dynamics simulations of various types of chemical entities say for drugs or construction materials or for nuclear innovations etc. They discover new computational methodology, create workflows and apply chemistry/ physics/ engineering knowledge to draw rigorous, actionable conclusions from complex data. Use of necessary software packages e.g., MARTINI and GROMACS to perform simulation tasks is essentially regular.

Computational Mathematician

Computational Mathematicians work on probabilistic graphical models for integration into simulation scenarios, design/ develop/ use/ evaluate mathematical models, design/ maintain/ enhance complex and diverse software algorithms that require mathematical intervention. Such roles involve postulating hypotheses and guiding / conducting effective experimentation to validate the hypotheses. They need experience in applying statistical and stochastic approaches in the design and analysis of experiments too.

Computational scientist - Bioinformatics

Bioinformaticians are involved in modeling of complex biological systems; multiplexed, trackable editing of cells / cell components; gene / genetic material editing etc. to address several genomic, proteomic, experimental design using mathematical modeling, simulation, data science, machine learning, and other computationally challenging techniques. They perform custom analyses of a wide range of data gathered from various instruments. They especially need experience of working with large data sets consisting of >1000s of samples.

Computational Scientist - Earth System Analysis / Geosciences, Atmospheric Sciences

Computational Scientists may also specialize in Atmospheric Sciences, Geosciences / Earth Systems analyses, Planetary / Exoplanetary modeling, weather prediction, water cycles/climate extremes modeling, energy/ecology. They may work to improve the numerical accuracy and computational efficiency of numerical models used in such analyses.

Computational Physicist

Computational Physicists basically focus on semiconductor device physics, plasma science & technology, microfluidics, MEMSes (micro-electromechanical systems), optical instruments/device components, lasers, radiation modeling etc. They also need hands-on knowledge of porting software to emerging computing devices, making software available on the cloud and object-oriented development (with C++, Python etc.).

Computational Fluid Scientist (CFD)

The most popular application of Fluid Mechanics in today’s market is in the petrochemical industry that focuses on exploration, development, production and marketing and transportation of hydrocarbons where Scientists handle challenging practical fluid mechanics problems using CFD software, numerical simulations, and programming skills to tackle various flow modeling challenges in, but not limited to, reservoir, wellbore, and surface equipment. Another very popular and interesting field of CFD is aeronautics and astronautics where there are significant challenges for CFD experts too. Professionals are PhD holders in Engineering, Computational Mechanics or Applied Mathematics among other related areas of study.

Computational Biologist

This will include pathology, metabolomics, systems biology, neurosciences or other branches of Life Sciences which are prominent in the healthcare industry as the Computational Biologists especially find work in the healthcare sector. Scientists focus on molecular profiling and mathematical modeling of biological and disease processes and interpret the output of such analyses in the clinical context. This includes but is not limited to computational analysis of high-dimensional, cell & molecular profiling data to advance precision medicine (patient based customized medicine) technology; identify somatic mutations (not affecting the brain) etc.

Computational Toxicologist – Drugs & Chemical / Biological Weaponry

Some Computational Toxicologists apply modeling approaches to understand and improve drug de-risking (reduce the risks associated with newly designed drugs) and safety prediction (predict how safe a newly developed drug is) in support of early pipeline decision making for newly developed drugs which are yet to be approved and released for marketing. They use probabilistic & machine learning models to predict potential off-target activities of drugs and provide new mechanistic hypotheses. This helps in taking important business decisions. They must extract large internal & external datasets, for data handling/ processing/ model building. Some others work in defense organizations to help solve some of the most critical and challenging problems facing the artillery market today. Professionals are PhD holders in Toxicology, Chemistry or related disciplines. Most recruiters prefer experience in programming, software development, data analysis/visualization, or machine learning related to structure-based toxicity or drug design.

Computational Scientist - Immunobiology / Immunotherapy / Antibody Engineering

The dominant industry for Computational Immunobiologists is healthcare. This includes predominantly immunotherapy & antibody engineering, a variety of immuno-assays, integrating clinical data, and then ensuring rapid computational analyses, analyze internal & external datasets to determine effective T cell therapeutics with a focus on the analysis of data from genomic, transcriptomic, or proteomic studies. They analyze and interpret high-throughput (automation of experiments such that large scale repetition becomes feasible) clinical DNA and RNA sequencing data with a focus on developing successful immunotherapies.

Career Growth

If you join as a Computational Scientist or Data Scientist or Decision Scientist or Staff Scientistor Scientist I or Computational Scientific Programmer after your post-graduation or Bachelor + professional certificate, you may move into roles like Associate Scientist or Assistant Modeler or Scientist II or similar.

The next levels of promotion will proceed as Senior Scientist, Lead Scientist or Data Scientist Technical Lead, Associate, Principal Scientist, Lab Head or Senior Principal Scientist.

Thereafter you may be promoted to roles such as Chief Scientist; Innovation Head; Senior Science Advisor; then Associate Scientific Director; Director - Scientific Data Analysis or Director- Computational Design or  Director Scientific Computing or  Director- Decision Sciences or  Director of Quantitative Research; VP- Data Science or  VP- Head of Computational Sciences or similar.

Salary Offered  

1. After your postgraduation, at entry-level jobs, you may expect to make about Rs. 50,000 – 1, 50,000 or even more a month. Higher salaries are paid to graduates from premier institutions.

2. Opportunities after a Bachelor degree are few and far between. However, it may be possible with a Bachelor degree plus some professional certificates endorsing your skills for common industry software or simulation techniques relevant to your field of education.

3. At the junior level jobs, you may expect to get about Rs. 60,000 – 2, 50,000 or even more per month.

4. In mid-level jobs in India, may vary from Rs. 1, 00,000 – 5, 00,000 per month or even more.

5. At the senior level, you may earn about Rs. 2, 50,000 –8, 00,000 or even more a month. Associate Scientific Directors or Directors of Quantitative Research or VP- Head of Computational Sciences may earn about Rs. 50, 00,000 to 3,00,00,000 or much more per year.

Global (US)

1. At Entry level as an Intern or Trainee, you may earn about $4,000 - $7,000 per month.

2. At Junior-level, with an experience of 1-5 years, you may earn around $5,000 - $11,500 or more per month.

3. At Mid-level, with an experience of 6-12 years, you may earn around $6,500 - $14,500 per month.

4. At Senior-level, with an experience of 13-20 + years, you may earn around $ 8,500 - $18, 000 per month.

Monthly Earnings In Indian Rupee

1. Entry level: 0 - 2 years of work experience 

2. Junior Level: From 1 to 12 years of work experience 

3. Mid-Level: From 5 to 20+ years of work experience 

4. Senior Level: From 10 to 25+ years of work experience (there could be exceptions in some high-end technical, financial, engineering, creative, management, sports, and other careers; also in the near future, people will reach these levels much faster in many careers and in some careers, these levels will have no meaning as those careers will be completely tech skill driven such as even now, there is almost no level in a Cyber Security Expert’s job)

Famous Personalities 

Information not available.

Work Activities 

1. Analyzing and interpreting data and information - Analysis of data and information to find facts, trends, reasons behind situations, etc.; interpretation of data to aid in decision making.

2. Computing - using various computer software applications; writing algorithm for various computer and mobile applications; using software applications for scientific and technical work.

3. Creative thinking - Developing new ideas, concepts, innovative solutions to problems, newer ways of getting things done, designing products and services, creating work of art and craft, etc.

4. Getting Information and learning - Observing, hearing, reading, using computers, or otherwise obtaining information and learning from it.

5. Making decisions and solving problems - Analysis of data and information; evaluation of alternative decisions and results of decisions; taking the right decisions and solving problems.

6. Organizing, planning and prioritizing tasks - Planning and organizing tasks in order to achieve work goals; prioritizing tasks to achieve goals and making the best use of the time available.

7. Processing Information - Compiling, tabulating, calculating, auditing, verifying or otherwise dealing with information processing including data entry, transcription, recording, storing and maintaining databases.

8. Strategic planning - Developing visions and goals, developing strategies and action plans for achieving visions and goals.

9. Updating and using relevant knowledge - Keeping updated with the latest knowledge relevant to your fields of work and use of the relevant knowledge in getting things done.

10. Using computers for work - Using computers for day-to-day office work; using computer software for various applications in day-to-day professional work; entering data and process information; for writing.

11. Working in a team - Working in a team of people; developing team; maintaining professional relationships among team members.