1

Biomedical Data Science Jobs in Washington, DC (NOW HIRING)

Principal Data Scientist

Gaithersburg, MD · On-site

$175K - $215K/yr

... turn complex biomedical and clinical data into defensible decisions for drug discovery and ... Translate complex data science findings into clear, actionable insights for non-technical ...

(ID: 2026-2841) Axle Informatics is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to ...

(ID: 2026-2841) Axle Informatics is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to ...

(ID: 2026-2841) Axle Informatics is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to ...

Showing results 21-40

Biomedical Data Science information

See Washington, DC salary details

$26.3K

$119.4K

$209.4K

How much do biomedical data science jobs pay per year?

As of Sep 7, 2026, the average yearly pay for biomedical data science in Washington, DC is $119,396.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,419.00 and $169,102.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a biomedical data scientist?

To thrive as a Biomedical Data Scientist, you need a strong background in statistics, machine learning, programming (typically Python or R), and a solid understanding of biological or clinical data. Familiarity with bioinformatics tools, data visualization platforms, high-throughput sequencing technologies, and relevant certifications (such as in data science or bioinformatics) is commonly required. Strong problem-solving abilities, communication skills, and interdisciplinary collaboration help set top professionals apart in this field. These competencies are crucial for extracting meaningful insights from complex biomedical data, driving research innovation, and supporting evidence-based healthcare decisions.

How does a biomedical data scientist typically collaborate with clinicians and researchers on interdisciplinary projects?

Biomedical Data Scientists often work closely with clinicians, biologists, and other researchers to translate complex biomedical questions into data-driven solutions. This collaboration usually involves regular meetings to understand clinical needs, define project goals, and discuss data interpretation. Effective communication is key, as team members may have different expertise and perspectives. By collaborating, Biomedical Data Scientists help ensure that analytical methods and results are both rigorous and clinically relevant, ultimately contributing to impactful healthcare outcomes.

What is the difference between Biomedical Data Science vs Bioinformatics?

AspectBiomedical Data ScienceBioinformatics
Required CredentialsDegree in Data Science, Biostatistics, or related fields; programming skillsDegree in Bioinformatics, Computational Biology, or related fields; programming skills
Work EnvironmentResearch labs, healthcare institutions, biotech companiesResearch labs, academic institutions, biotech firms
Industry UsageAnalyzing large biomedical datasets, developing predictive modelsAnalyzing biological data, genome sequencing, gene annotation
Search & Comparison IntentHigh overlap in data analysis, healthcare applicationsFocus on biological data interpretation

Biomedical Data Science and Bioinformatics share many skills and work environments, but they differ in focus. Biomedical Data Science emphasizes analyzing large datasets and developing predictive models in healthcare, while Bioinformatics concentrates on biological data analysis, such as genome sequencing. Both roles require programming skills and are vital in biomedical research, but their specific applications and industry terminology vary.

Can you become a biomedical data scientist with a biomedical science degree?

A biomedical data scientist typically has a background in biomedical science combined with skills in data analysis, programming, and statistics. While a biomedical science degree provides a strong foundation, additional training in programming languages like Python or R and experience with data management are often necessary to qualify for such roles.

What does a biomedical data scientist do?

A biomedical data scientist analyzes complex biological and medical data to identify patterns and insights that can improve healthcare and research. They use statistical methods, machine learning, and data visualization tools to interpret data from sources like electronic health records, genomic sequences, and clinical trials, often working in interdisciplinary teams and requiring programming skills in languages such as Python or R.
Infographic showing various Biomedical Data Science job openings in Washington, DC as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, and 2% Contract. Highlights an 80% Physical, 4% Hybrid, and 16% Remote job distribution, with an average salary of $119,396 per year, or $57.4 per hour.

Principal Data Scientist

BULLFROG AI MANAGEMENT LLC

Gaithersburg, MD • On-site, Remote

$175K - $215K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 29 days ago


Job description

Description

About BullFrog AI

BullFrog AI (NASDAQ: BFRG) is a computational biology company spun out of the Johns Hopkins Applied Physics Laboratory. We sit at the intersection of AI and drug discovery, building platforms that help pharmaceutical and biotech companies make better, faster decisions across the drug development lifecycle:

  • bfPREP - biology-aware data harmonization that unlocks the value trapped in fragmented, multi-site clinical and omics datasets
  • bfLEAP - causal AI analytics for patient subgroup discovery, biomarker identification, and drug target prioritization
  • bfARENAS - structured multi-criteria decision support for high-stakes portfolio, indication, and go/no-go decisions


The Role

Help turn complex biomedical and clinical data into defensible decisions for drug discovery and development.


BullFrog AI is seeking an exceptional Principal Data Scientist with a strong background in biomedical analytics. This is a senior individual contributor role that combines client-facing project leadership with direct contributions to BullFrog's in-house platform - spanning target discovery, patient subtyping, and multimodal data analysis.


Day-to-day work is client-dependent, with each engagement bringing a different analytical challenge. You will be expected to lead from execution and bring scientific rigor, technical depth, and clear communication to every project.



What You'll Do


Client Engagement & Project Leadership

  • Own end-to-end analytical workstreams across target discovery, patient subtyping, and multimodal data exploration
  • Translate complex data science findings into clear, actionable insights for non-technical stakeholders and senior leadership
  • Collaborate effectively with distributed, remote teams to ensure seamless communication and on-time delivery


Technical Delivery

  • Design, implement, and optimize AI/ML models using diverse healthcare data types, including omics, claims, and EHR
  • Conduct multi-omic analyses such as RNA-seq differential expression, clustering, genomic association analysis, and biomarker discovery
  • Implement NLP and LLM solutions tailored to life sciences datasets
  • Contribute to the development and refinement of BullFrog's in-house platform capabilities

Requirements

What We're Looking For


Education

Advanced degree (MS or PhD) in a quantitative field - biology, computer science, engineering, or related


Experience

5+ years of post-graduate experience in life sciences analytics in pharma, biotech, or consulting

Demonstrated proficiency in Python; production-quality code in a research or commercial environment

Hands-on experience with major healthcare data types - omics data required; claims and EHR experience strongly preferred

Hands-on experience applying LLMs or NLP to real analytical workflows, ideally biomedical or clinical text extraction, summarization, evaluation, or retrieval

Proven track record of collaborating effectively with remote teams

Must be legally authorized to work in the United States


Desired Skills

Prior experience in multi-modal analysis with deep learning

Causal inference experience - a significant differentiator for this role

NLP/LLM expertise applied to life sciences and healthcare text

Strong foundation in data engineering and databases

Experience with graph-based approaches: feature engineering, ML, and visualization

Cloud platform experience, particularly AWS or Google Cloud


Who Will Thrive Here

Intellectually versatile - energized by moving across problem types, data modalities, and scientific domains

Client-ready - comfortable presenting to and partnering with senior scientific and business stakeholders

Independently driven - able to scope, execute, and deliver complex analyses without heavy oversight

Scientifically rigorous - you hold your work to a publication-quality standard even in a commercial environment

Collaborative by default - remote-first is second nature; you communicate proactively and close loops


What We Offer

Base salary range: $175,000 - $210,000 per year, based on level of education, years of relevant experience, skills, and location. This role is eligible for performance bonus and equity/stock options, subject to company and plan terms. Benefits include medical, dental, and vision from day one, short-term disability, 401(k) from first paycheck, 15 days PTO, 11 paid holidays annually, and maternity and paternity leave. 
BullFrog AI is an equal opportunity employer. We are committed to building a diverse team that reflects the communities and patients our work ultimately serves. 
Advancing medicine through artificial intelligence.