Data Scientist - Portfolio Optimization

Описание вакансии

About Formation Bio

*Formation 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, Sam Altman, John Doerr, Spark Capital, SV Angel Growth, and others.

You can read more at the following links:

• Our Vision for AI in Pharma
• Our Current Drug Portfolio
• Our Technology & Platform

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 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

• 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

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.

Formation Bio is prioritizing hiring in key hubs, primarily the New York City and Boston metro areas. These positions will follow a hybrid work model with 1-3 days required at the office. *Please only apply if you reside in these locations or are willing to relocate.*

Salary ranges are informed by a number of factors including geographic location. The range provided includes base salary only. In addition to base salary, we offer equity, comprehensive benefits, generous perks, hybrid flexibility, and more. If this range doesn't match your expectations, please still apply because we may have something else for you.

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

You will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.