Data Scientist, Portfolio Optimization

Data Scientist, Portfolio OptimizationNew York, NY; Boston, MAAbout Formation BioFormation Bio is a tech and AI driven pharma company differentiated by radically more efficient drug development.Advancements in AI and drug discovery are creating more candidate drugs than the industry can progress because of the high cost and time of clinical trials. Recognizing that this development bottleneck may ultimately limit the number of new medicines that can reach patients, Formation Bio, founded in 2016 as TrialSpark Inc., has built technology platforms, processes, and capabilities to accelerate all aspects of drug development and clinical trials. Formation Bio partners, acquires, or in-licenses drugs from pharma companies, research organizations, and biotechs to develop programs past clinical proof of concept and beyond, ultimately helping to bring new medicines to patients. The company is backed by investors across pharma and tech, including a16z, Sequoia, Sanofi, Thrive Capital, John Doerr, Spark Capital, SV Angel Growth, and others.At Formation Bio, our values are the driving force behind our mission to revolutionize the pharma industry. Every team and individual at the company shares these same values, and every team and individual plays a key part in our mission to bring new treatments to patients faster and more efficiently.About the PositionAs 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.ResponsibilitiesWork with the team to implement and maintain core portfolio engine: order management system, execution simulation layer, portfolio construction service, and performance trackingDesign risk frameworks that quantify exposure across a portfolio of drug development bets with radically different risk profiles, timelines, and failure modesRun rigorous backtesting experiments with strict temporal constraints to evaluate Formation strategies against baseline approaches and measure marginal signal from new evidence sourcesCoordinate across the organization to integrate internal Formation data sources (clinical trial data, genomic evidence, real-world data) and proprietary tooling into portfolio analytics pipelinesWork with product and engineering teams to build dashboards and reporting that communicate portfolio performance, risk metrics, and strategy comparisons to both technical and executive stakeholdersCollaborate with the broader data science team to ensure portfolio-level evaluation feeds back into model improvement and evidence prioritizationAbout YouRequired QualificationsPhD 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 comparisonDemonstrated ability to work with messy, real-world datasets — comfortable with data wrangling, deduplication, and quality assessmentClear communicator who can present quantitative results to both technical peers and business stakeholdersPreferred QualificationsExperience 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 contextExperience with probability-of-success modeling, drug development decision analysis, or health economicsComfort with LLMs or AI/ML pipelines in a production or research settingFamiliarity 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 - $202,000Compensation Individual compensation is determined by several factors, including role scope, geographic location, and skills & experience. Your offer will reflect where you fall within the range based on these considerations. In addition to base salary, we offer equity, comprehensive benefits, and generous perks. If the posted range doesn't match your expectations, we still encourage you to apply!Where We Hire Formation Bio is prioritizing hiring in key hubs, primarily the New York City and Boston metro areas, with a hybrid model requiring 3 days per week in office. Applicants from the Research Triangle (NC) and San Francisco Bay Area may also be considered. Please apply only if you reside in these locations or are willing to relocate.Equal Opportunity Formation Bio is committed to building a diverse and inclusive team. We are an equal opportunity employer and welcome candidates from all backgrounds. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, national origin, ancestry, sex (including pregnancy, childbirth, breastfeeding, and related medical conditions), gender identity or expression, sexual orientation, age, disability, genetic information, marital status, military or veteran status, or any other characteristic protected by federal, state, or local law.