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Computational Data Science Jobs (NOW HIRING)

Formation Bio

Director, Data Science

Boston, NY

$235K - $307K/yr

You'll work at the intersection of computational biology, machine learning, and drug development ... Lead and execute complex data science projects that directly advance our drug development portfolio

LSU AgCenter

Assistant/Associate Professor (Computational Biology/Data Science) Position Type:Faculty Department:LSUAG PL1 - Department of Plant Pathology and Crop Physiology (Lawrence E Datnoff (00013100)) Work ...

Louisiana State University

Assistant/Associate Professor (Computational Biology/Data Science) Position Type:Faculty Department:LSUAG PL1 - Department of Plant Pathology and Crop Physiology (Lawrence E Datnoff (00013100)) Work ...

Louisiana Workforce Commission

Assistant/Associate Professor (Computational Biology/Data Science) Position Type:Faculty Department:LSUAG PL1 - Department of Plant Pathology and Crop Physiology (Lawrence E Datnoff (00013100)) Work ...

Caltech

Data Science Engineering

Pasadena, CA · On-site

$40.50 - $64/hr

Develop computational and data-driven methods supporting research in algebraic geometry, computational algebra, and related areas of mathematics and scientific computing * Contribute to the design ...

MSD

Senior Specialist, Data Science

San Francisco, CA · On-site

$129K - $203K/yr

The Computational Toxicology Group within Nonclinical Drug Safety (NDS) seeks a senior AI/ML ... Computational Biology, Computational Chemistry, Data Engineering, Data Modeling, Data Science, Data ...

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How much do computational data science jobs pay per hour?

As of Jun 19, 2026, the average hourly pay for computational data science in the United States is $56.81, according to ZipRecruiter salary data. Most workers in this role earn between $46.63 and $67.31 per hour, depending on experience, location, and employer.

What does a computational data scientist do?

A computational data scientist analyzes large datasets using programming languages like Python or R, develops algorithms, and applies statistical models to extract insights and solve complex problems. They often work with machine learning tools and require strong analytical skills to support data-driven decision-making in organizations.

What is the difference between Computational Data Science vs Data Analyst?

AspectComputational Data ScienceData Analyst
Required CredentialsTypically requires a degree in Computer Science, Data Science, or related fields; often includes programming certificationsUsually requires a degree in Statistics, Business, or related fields; may include basic data analysis certifications
Work EnvironmentInvolves programming, modeling, and developing algorithms; often in tech or research settingsFocuses on interpreting data, creating reports, and supporting decision-making; in business or corporate environments
Employer & Industry UsageUsed in tech companies, research institutions, and industries requiring advanced modelingCommon in finance, marketing, healthcare, and business sectors

Computational Data Science involves advanced programming, algorithm development, and modeling, often in technical environments. Data Analysts focus on interpreting data, generating reports, and supporting business decisions. While both roles work with data, Computational Data Scientists typically require stronger programming skills and work on building models, whereas Data Analysts focus on data interpretation and visualization.

What is Computational Data Science?

Computational Data Science is an interdisciplinary field that combines computer science, statistics, and domain knowledge to extract insights and knowledge from complex data sets using computational techniques. Professionals in this field use algorithms, machine learning, and advanced analytics to solve real-world problems by processing and interpreting large volumes of data. The work often involves programming, data modeling, and visualization, making it crucial in industries such as healthcare, finance, and technology. Computational Data Scientists help organizations make data-driven decisions and innovate through predictive modeling and data analysis.

Is 40 too late for data science?

Computational Data Science is a field where individuals can enter at any age, as success depends on skills, experience, and continuous learning. Many professionals transition into data science later in their careers by acquiring relevant knowledge in programming, statistics, and machine learning through online courses or certifications.

What are some common challenges faced by computational data scientists when working on cross-functional teams?

Computational data scientists often collaborate closely with professionals from diverse backgrounds, such as software engineers, domain experts, and business stakeholders. One common challenge is translating complex technical findings into actionable insights for non-technical team members. Additionally, aligning project goals and expectations across disciplines can require extra communication and flexibility. Overcoming these challenges often involves developing strong interpersonal skills, proactively clarifying requirements, and fostering a collaborative team culture.

Which is better, DS or CS?

Computational Data Science and Computer Science are related fields, but Data Science focuses on analyzing and interpreting data using statistical and machine learning techniques, while Computer Science emphasizes algorithms, programming, and software development. The choice depends on your career goals; Data Science roles often require skills in statistics, data visualization, and tools like Python or R, whereas Computer Science roles may focus more on software engineering and systems design.

What are the key skills and qualifications needed to thrive as a Computational Data Scientist, and why are they important?

To thrive as a Computational Data Scientist, you need a strong background in mathematics, statistics, programming (especially Python or R), and data analysis, often supported by a relevant degree in computer science, statistics, or a related field. Proficiency with data manipulation tools (like Pandas, NumPy), machine learning frameworks (such as TensorFlow or Scikit-learn), and cloud computing platforms is highly valued, along with experience using data visualization tools. Critical thinking, problem-solving, communication, and collaboration skills make someone stand out in this role. These abilities are crucial for extracting actionable insights from complex data, building effective models, and communicating findings to drive informed business decisions.

Is computational science a good career?

Computational Data Science is a growing field that combines programming, statistical analysis, and domain knowledge to solve complex problems using data. It offers opportunities in industries such as technology, finance, healthcare, and research, often requiring skills in machine learning, data visualization, and programming languages like Python or R. The career typically involves continuous learning and can provide competitive salaries and job stability.
More about Computational Data Science jobs
What cities are hiring for Computational Data Science jobs? Cities with the most Computational Data Science job openings:
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Computational Data Science jobs near you
Infographic showing various Computational Data Science job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 90% Full Time, and 9% Part Time. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $118,171 per year, or $56.8 per hour.
Bioinformatics Programmer 3

Bioinformatics Programmer 3

University of California San Francisco

San Francisco, CA • On-site

Full-time

Posted 5 days ago

Job description

Job Description
A cover letter is required for this role.
The Kidney Genetics, Genomics, and Advanced Models Laboratory at the UCSF Mission Bay campus is recruiting an individual with expertise in computational biology.
The general responsibilities of this position are as follows:
  • Independent analysis of single cell and bulk genomics data including from CRISPR screens, kidney organoid models, and primary cell models. Datasets generated and used by the laboratory include single cell and bulk RNA-seq, single cell and bulk ATAC-sequencing, ChiP-sequencing, and HiC.
  • Analysis of biobank scale human genetics data to identify novel genotype-phenotype and gene-environment interactions.
  • Figure generation and writing for publications and grant proposals
  • Coordination and management of laboratory compute resources

Candidates will be expected to work independently and in a collaborative multidisciplinary team. Opportunities for career development will be provided and encouraged. The candidate will report to Gabriel Loeb, MD, PhD, the Principal Investigator of the Kidney Genetics, Genomics, and Advanced Models Laboratory.
Department Description:
The Nephrology Division spans multiple campuses with faculty and staff located at the UCSF Health, San Francisco Veterans Affairs Medical Center, and Zuckerberg San Francisco General The Nephrology Division consists of faculty, fellows, and staff whose work is grounded in deep commitment to and respect for patients with kidney disease.
The Nephrology Division has employed and trained leaders in academic nephrology, superb clinical research scientists and basic scientists, and outstanding clinical nephrologists. Our faculty are leading experts in adult nephrology: including acute kidney injury, chronic kidney disease, hypertension, electrolyte disorders, and kidney transplantation.
Our mission continues in this tradition: expanding the frontiers of basic and clinical investigation in nephrology, training the next generation of academic nephrology leaders and providing the highest level of patient care. Our division within and the UCSF School of Medicine overall
Qualifications
Required qualifications:
  • Bachelor's degree in Bioinformatics/ Systems Biology/ Math/ Statistics/ Computer / Computational / Data Science, or Domain Sciences with computer / computational / data specialization or equivalent experience / training.
  • 3 or more years of directly relevant programming experience
  • Proficiency in at least one programming language used for single-cell analysis (e.g., R or Python), including use of established single-cell analysis frameworks.
  • Thorough knowledge of bioinformatics methods, applications programming, web development and data structures.
  • Thorough knowledge of bioinformatics programming design, modification and implementation.
  • Independently design and execute end-to-end single-cell analysis pipelines.
  • Strong project management skills.
  • Ability to generate publication-quality figures and clearly communicate analytical results in writing.
  • Demonstrated ability to work independently and collaboratively within a multidisciplinary research team.
  • Make and defend methodological choices for normalization, integration, and statistical testing
  • Partner closely with experimentalists to shape data generation and interpret biological findings.
  • Thorough knowledge of modern biology and applicable field of research.
  • Communication skills to work with both technical and non-technical personnel in multiple fields of expertise and at various levels in the organization.
  • Ability to communicate technical information in a clear and concise manner.
  • Ability to interface with management on a regular basis.
  • Self motivated, work independently or as part of a team, able to learn quickly, meet deadlines and demonstrate problem solving skills.
  • Thorough knowledge of web, application and data security concepts and methods.

Preferred qualifications:
  • PhD degree in Bioinformatics/ Systems Biology/ Math/ Statistics/ Computer / Computational / Data Science, or Domain Sciences with computer / computational / data specialization or equivalent experience. Strong preference for those with experience in advanced single cell sequencing and genomewide association data analyses.Substantial experience with single-cell data integration, batch correction, and multi-modal analysis.
  • Experience analyzing perturbation-based single-cell datasets, including CRISPR or pooled screens.
  • Experience working with organoid or disease-model single-cell datasets.
  • Familiarity with scalable or high-performance computing environments for large single-cell datasets.
  • Experience with bulk genomics and human genetics data

About Us
About UCSF
The University of California, San Francisco (UCSF) is a leading university dedicated to promoting health worldwide through advanced biomedical research, graduate-level education in the life sciences and health professions, and excellence in patient care. It is the only campus in the 10-campus UC system dedicated exclusively to the health sciences. We bring together the world's leading experts in nearly every area of health. We are home to five Nobel laureates who have advanced the understanding of cancer, neurodegenerative diseases, aging and stem cells.
Pride Values
UCSF is a diverse community made of people with many skills and talents. We seek candidates whose work experience or community service has prepared them to contribute to our commitment to professionalism, respect, integrity, diversity and excellence - also known as our PRIDE values.
In addition to our PRIDE values, UCSF is committed to equity - both in how we deliver care as well as our workforce. We are committed to building a broadly diverse community, nurturing a culture that is welcoming and supportive, and engaging diverse ideas for the provision of culturally competent education, discovery, and patient care. Additional information about UCSF is available here.
Join us to find a rewarding career contributing to improving healthcare worldwide.
Equal Employment Opportunity
The University of California is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected status under state or federal law.
Salary Information
The final salary and offer components are subject to additional approvals based on UC policy.
Your placement within the salary range is dependent on a number of factors including your work experience and internal equity within this position classification at UCSF. For positions that are represented by a labor union, placement within the salary range will be guided by the rules in the collective bargaining agreement.
To learn more about the benefits of working at UCSF, including total compensation, please visit: https://ucnet.universityofcalifornia.edu/compensation-and-benefits/index.html

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