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Biotech Data Science Jobs in New York (NOW HIRING)

Formation Bio

Senior Data Scientist

New York, NY · Hybrid

$177K - $232K/yr

Lead and execute complex data science projects that directly advance our drug development portfolio ... biotech, pharma, consulting) * Strong programming skills, particularly in Python * Extensive ...

Formation Bio

Principal Data Scientist

New York, NY

$204K - $267K/yr

Lead and execute complex data science projects that directly advance our drug development portfolio ... biotech, pharma, consulting) * Strong programming skills, particularly in Python * Extensive ...

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Biotech Data Science information

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$41K

$134.3K

$215K

How much do biotech data science jobs pay per year?

As of Aug 2, 2026, the average yearly pay for biotech data science in New York is $134,280.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,800.00 and $148,800.00 per year, depending on experience, location, and employer.

Can data scientists make $300k?

Biotech data scientists can potentially earn $300,000 or more annually, especially with extensive experience, advanced skills in machine learning and bioinformatics, and working in senior or specialized roles. Compensation varies based on location, company size, and individual expertise, with some senior-level positions reaching or exceeding this salary level.

What are the most common challenges faced by professionals in Biotech Data Science roles?

One of the primary challenges in Biotech Data Science is working with large, complex, and sometimes incomplete biological datasets, which require advanced analytical approaches and careful data curation. Professionals often need to stay current with rapidly evolving technologies and methods, which can be demanding but also rewarding for those who enjoy continuous learning. Collaboration with scientists, engineers, and regulatory teams is common, so adapting communication styles and translating technical findings to diverse audiences is key. Overcoming these challenges leads to meaningful scientific discoveries and significant career growth opportunities.

What are the key skills and qualifications needed to thrive in the Biotech Data Science position, and why are they important?

To thrive in Biotech Data Science, you need a solid background in biology or biotechnology, strong statistical and analytical skills, and experience with data analysis languages like Python or R. Familiarity with bioinformatics tools, sequencing platforms, and data visualization software is often expected, with certifications in data science or related fields considered a plus. Excellent problem-solving, communication, and collaboration skills are essential when working across multidisciplinary teams. These competencies enable effective interpretation of complex biological data, driving innovation and insights in the biotech industry.

What is a biotech data scientist?

A biotech data scientist analyzes biological and medical data to support research and development in the biotechnology industry. They use skills in statistics, programming, and machine learning, often working with tools like Python, R, and SQL to interpret complex datasets and inform decision-making.

What is a Biotech Data Science job?

A Biotech Data Science job involves analyzing complex biological and pharmaceutical data to drive research, innovation, and decision-making. Professionals in this field use machine learning, statistical modeling, and bioinformatics tools to extract insights from genomics, clinical trials, and drug discovery datasets. They collaborate with scientists, engineers, and healthcare professionals to improve treatments, develop new therapies, and optimize bioprocesses. Strong programming skills, domain knowledge in biology or biotechnology, and expertise in data analysis are essential for success in this role.

How can data science be used in biotechnology?

Biotech data scientists analyze large biological datasets to identify patterns, develop predictive models, and optimize processes such as drug discovery, genetic research, and personalized medicine. They use tools like machine learning, statistical analysis, and bioinformatics software to support research and development efforts in biotechnology companies and labs.

Can a biotechnologist become a data scientist?

A biotechnologist can become a data scientist by acquiring skills in programming, statistics, and machine learning, often through additional training or education such as online courses or advanced degrees. Their background in biology and laboratory data can provide a strong foundation for analyzing complex datasets in data science roles within biotech and healthcare industries.
What are the most commonly searched types of Biotech Data Science jobs in New York? The most popular types of Biotech Data Science jobs in New York are:
What are popular job titles related to Biotech Data Science jobs in New York? For Biotech Data Science jobs in New York, the most frequently searched job titles are:
What job categories do people searching Biotech Data Science jobs in New York look for? The top searched job categories for Biotech Data Science jobs in New York are:
What cities in New York are hiring for Biotech Data Science jobs? Cities in New York with the most Biotech Data Science job openings:
Biotech Data Science jobs near you
Infographic showing various Biotech Data Science job openings in New York as of July 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Remote job distribution, with an average salary of $134,280 per year, or $64.6 per hour.

Data Scientist, Portfolio Optimization

Formation Bio

New York, NY

$154K - $202K/yr

Other

Re-posted 9 days ago

Job description

About the Position 

As a Data Scientist on the platform prediction team, you'll translate our probability of success predictions into measurable portfolio-level outcomes. You'll architect core systems - order management, execution simulation, portfolio construction, risk monitoring, and performance attribution - that let us rigorously evaluate signals from our AI-driven predictions in public and private equities and our internal portfolio.

This role sits at the intersection of quantitative finance, healthcare data, and AI-driven drug development. If you're excited about applying portfolio construction and risk management fundamentals to one of the most consequential prediction problems in healthcare, this is the role.No other company - hedge fund or pharma - has a technical data science position translating drug development experience into durable AI-native portfolio strategies. The skills you develop here - portfolio construction over assets with radically asymmetric risk profiles, clinical trial analytics, AI/ML in production, and risk management across multi-year horizons - can directly impact the delivery of new and effective therapeutics to patients by best aligning impactful medicines with economic incentives.

Responsibilities

  • Work with the team to implement and maintain core portfolio engine: order management system, execution simulation layer, portfolio construction service, and performance tracking
  • Design risk frameworks that quantify exposure across a portfolio of drug development bets with radically different risk profiles, timelines, and failure modes
  • Run rigorous backtesting experiments with strict temporal constraints to evaluate Formation strategies against baseline approaches and measure marginal signal from new evidence sources
  • Coordinate across the organization to integrate internal Formation data sources (clinical trial data, genomic evidence, real-world data) and proprietary tooling into portfolio analytics pipelines
  • Work with product and engineering teams to build dashboards and reporting that communicate portfolio performance, risk metrics, and strategy comparisons to both technical and executive stakeholders
  • Collaborate with the broader data science team to ensure portfolio-level evaluation feeds back into model improvement and evidence prioritization

About You 

Required Qualifications

  • PhD in a quantitative field (statistics, finance, physics, computational science, engineering, or related)
  • 1-3 years in a quantitative research, data science, or analytics role in life sciences or life science adjacent field (healthcare, academic research, or consulting all count; substantive internships qualify)
  • Strong Python programming skills with experience in data-intensive workflows (pandas, numpy, scipy)
  • Solid grasp of core portfolio construction and risk concepts: position sizing, rebalancing, Sharpe ratio, drawdown, volatility, benchmark comparison
  • Demonstrated ability to work with messy, real-world datasets - comfortable with data wrangling, deduplication, and quality assessment
  • Clear communicator who can present quantitative results to both technical peers and business stakeholders

Preferred Qualifications

  • Experience with backtesting frameworks or portfolio simulation (vectorbt, Backtrader, or custom implementations)
  • Exposure to healthcare, pharma, or biotech data (clinical trials, claims data, -omics, real-world evidence)
  • Familiarity with alternative data in a research or investment context
  • Experience with probability-of-success modeling, drug development decision analysis, or health economics
  • Comfort with LLMs or AI/ML pipelines in a production or research setting
  • Familiarity with dashboard/visualization tools (Streamlit, Plotly, Dash) and pipeline orchestration (Dagster, Airflow)

Healthcare OR finance domain knowledge is valued; both are not required.

Total Compensation Range: $154,500 - $202,000

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