Data Scientist, Portfolio Optimization
Core
Translate probability of success predictions into measurable portfolio-level outcomes for drug development assets.
Role type
Data Scientist (Portfolio Optimization & Risk)
Builds
Portfolio engine systems including order management, execution simulation, portfolio construction, and risk monitoring.
Domain
Quantitative finance + AI-driven drug development
Deliverable
production ML models | product features | dashboards & analysis
Required skills
Python (pandas, numpy, scipy), portfolio construction, risk management, data wrangling, backtesting, quantitative research
Preferred skills
backtesting frameworks (vectorbt, Backtrader), healthcare/pharma data, alternative data, probability-of-success modeling, LLMs/AI/ML pipelines, dashboard tools (Streamlit, Plotly, Dash), pipeline orchestration (Dagster, Airflow)
Technologies
Python, pandas, numpy, scipy, vectorbt, Backtrader, Streamlit, Plotly, Dash, Dagster, Airflow
Responsibilities
Implement and maintain core portfolio engine systems; Design risk frameworks for drug development bets; Run rigorous backtesting experiments; Integrate internal data sources into analytics pipelines; Build dashboards for portfolio performance and risk metrics; Collaborate on model improvement and evidence prioritization.
Seniority
Mid-level (1-3 years experience)