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Weekend Biotech Data Science Jobs in New York (NOW HIRING)

Arlyn AI

Principal / Senior Data Scientist

New York, NY · Remote

$175K - $255K/yr

You'll work at the intersection of AI, data science, and healthcare, helping biotech and pharma companies navigate patient access barriers and optimize commercial decisions. What You'll Do * Own the ...

Arlyn AI

Principal / Senior Data Scientist

Manhattan, NY · On-site

$175 - $255/hr

You'll work at the intersection of AI, data science, and healthcare, helping biotech and pharma companies navigate patient access barriers and optimize commercial decisions. What You'll Do * Own the ...

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Data Scientist, Portfolio Optimization

Formation Bio

New York, NY

$154K - $202K/yr

Full-time

Re-posted 22 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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