What is a Computational Biologist?

A computational biologist is a data-driven scientist who develops and applies new and existing computational methods to facilitate life science-based analysis, research and experiments. Computational biologists usually have a master’s or doctorate degree in a STEM discipline and at least four to five years of experience with data science or biological analytics.

 All-Purpose Scientist

The average computational biologist will conduct applied scientific research into everything from drug delivery to cancer diagnostic technologies to instrumentation methodology. They may develop data analysis methods and statistical machine learning tools to identify and isolate clinical, molecular and physiological disease markers. To accomplish this, they must build mechanistic models to understand large-scale sets of biological data. They may work with other data scientists regarding data analyses, algorithm development and investigative research.

Computational biologists may draw knowledge from the fields of physics, biology, chemistry, statistics and computer science to develop new technology solutions that help change the current detection, prevention and management of disease. Sample projects may include smart contact lens with micro glucose sensor and nano-diagnostics that help with early detection of heart disease. They work with clinicians and experimental scientists to drive and support research while working with software engineers to transform novel methods into large scale tools. Depending on the employer, they may be expected to have knowledge of oncology, cardiology or immunology.

Careers With a Software Company

A computational biologist who works in a software or consulting company may deal with consumer goods, the public sector and the pharmaceutical industry. They use data science to drive evidence-based decision making for unique solutions that improve profitability, streamline operations and reduce costs and errors. They work with life science professionals to understand challenges and opportunities to develop innovative computational biology approaches. The leverage a broad array of talents related to statistics, mathematics, modeling, simulation, data mining and machine learning.

These computational biologists may consume and process complex data structures in order to perform statistical data summarization and normalization analyses. They use advanced visualization techniques to create engaging reports. They may create new software based tools and applications in conjunction with labor and cost estimates for clients. They must have verified computational biology and data science experience. Employers look for candidates who have biotech, genetic, clinical and pharmaceutical science experience. They’ll need a deep understanding of data algorithms, processing and visualization as well as strong programming skills in scripting languages such as Perl and Python.

Medical Research

A medical research computational biologist will be a data scientist with a background in the life science industry or medical computational biology. They usually have five years of professional data analytics and algorithm development experience. They work alongside diverse teams of math- and science-based problem solvers. They may be involved in all phases of life science-based consumer products, from clinical trials to manufacturing support, according to Nature.

They apply their skills related to machine learning, statistical modeling and simulation analysis to develop creative and unique approaches to medical research problems. They must understand complex data structures, perform statistical analyses and use programs like Partek, Tableau, or Spotfire for conceptual visualization. After this process is complete, they create new software tools and detailed project plans. Experience with Java, C#, Unix, and Linux is desired.

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Computational biologists may work academia or associated industry settings as managers of postdoctoral interns, students and researchers. They must be comfortable with analyzing clinical, medical and translational data.