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Phd Biophysics Data Scientist Jobs (NOW HIRING)

Compunnel

PHD AI/ML Data Scientist

New York, NY · On-site

Required Qualifications • PhD in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, or a related quantitative discipline. • 8-12 years of hands-on ...

VTG

Data Scientist

Mclean, VA

Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related field (PhD preferred for some roles) * 5+ years of experience in data science or a related field

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How much do phd biophysics data scientist jobs pay per year?

As of Sep 10, 2026, the average yearly pay for phd biophysics data scientist in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What does a PhD biophysics data scientist do?

A PhD Biophysics Data Scientist applies advanced data analysis, machine learning, and computational modeling techniques to biological and physical data. They use their expertise in biophysics to interpret complex datasets, often working on problems such as protein structure prediction, drug discovery, or biomolecular simulations. Their work bridges the gap between experimental biology and computational science, enabling new insights and solutions in biomedical research.

What are the key skills and qualifications needed to thrive as a PhD biophysics data scientist, and why are they important?

To thrive as a PhD Biophysics Data Scientist, you need a strong background in biophysics, advanced quantitative analysis, and programming, typically supported by a PhD in a relevant field. Proficiency in statistical analysis tools (such as R or Python), machine learning frameworks, and experience with large-scale biological datasets are essential. Strong problem-solving abilities, effective communication, and interdisciplinary collaboration set top candidates apart. These skills enable accurate data-driven insights, effective teamwork, and innovation in solving complex biological problems.

What are some common interdisciplinary challenges faced by PhD biophysics data scientists when collaborating with experimental and computational teams?

PhD Biophysics Data Scientists often work at the intersection of biology, physics, and data science, requiring effective collaboration with both experimentalists and computational modelers. A common challenge is bridging the communication gap—translating complex biological or physical phenomena into data-driven models that are accessible to team members from varying backgrounds. Additionally, integrating heterogeneous datasets (such as imaging, sequencing, or simulation data) can be technically demanding and may require negotiating differences in data formats and standards. Overcoming these challenges often leads to more innovative solutions and provides valuable opportunities to deepen interdisciplinary expertise.

What are popular job titles related to Phd Biophysics Data Scientist jobs?

For Phd Biophysics Data Scientist jobs, the most frequently searched job titles are:

Infographic showing various Phd Biophysics Data Scientist job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Senior Data Scientist, Biologics Discovery

Raritan, NJ • On-site

Scorpion Therapeutics
51 - 200 employees
Learn more about Scorpion Therapeuticsexternal

$109K - $174K/yr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 5 days ago

Job description

About The Opportunity

Senior Data Scientist (Biologics Discovery) within Data, Data Science & AI (DDSAI), partnering with In Silico Discovery (ISD). Build data-facing ML capabilities (featurization, model-ready datasets, evaluation frameworks, applied models on assay and sequence data) to make ISD’s molecular property models faster and more trustworthy.

Position Summary

Design featurization and dataset curation, build/evaluate applied models on biologics assay, biophysical, and sequence/construct data, and define evaluation frameworks that keep models trustworthy. Shape how AI learns from every biologics experiment.

Key Responsibilities
  • Develop featurization and model-ready datasets from antibody/protein sequence, construct, assay, and biophysical data.
  • Collaborate with data engineers to define features/labels/aggregation levels; preserve raw representations.
  • Curate, document, and version datasets for reproducibility and traceability.
  • Partner with ISD to hand off standardized, traceable training datasets; align ownership boundaries.
  • Frame ML problems around real decision points in the design-make-test-learn (DMTL) cycle.
  • Work with ontology and MLOps teams to ensure consistent semantics and reliable model movement into use.
  • Champion reproducibility, documentation, and responsible AI.
Qualifications
  • Required: Master's/PhD (CS/ML/Computational Biology/Bioinformatics/Statistics or related); 2+ years applied ML (model development/evaluation/dataset curation) on scientific/biomedical data; Python (PyTorch/scikit-learn) and SQL; experience with heterogeneous experimental data features/training sets; understanding of evaluation/validation, leakage, and distribution shift; collaboration in matrixed R&D.
  • Preferred: Biologics/antibody-protein/protein language models; active learning/Bayesian optimization/generative sequence models; familiarity with biophysical/assay data and developability endpoints; MLOps/experiment tracking/model monitoring; ontologies/knowledge graphs for AI-ready datasets.
Compensation & Benefits (as stated)
  • Anticipated base pay: $109,000-$174,800;
  • annual performance bonus eligibility;
  • medical/dental/vision/life insurance and disability;
  • 401(k)/pension;
  • time off: vacation up to 120 hours/year, sick up to 40 hours/year, floating/holiday pay up to 13 days/year (plus Work/Personal/Family time up to 40 hours/year).
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