Data Scientist, Portfolio Optimization

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

Requirements

• MS or 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 — finance, 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

Nice-to-Haves

• 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)

Compensation & Benefits

• Total Compensation Range: $154,500 - $202,000
• Equity and comprehensive benefits package in addition to base salary