Data Scientist, Portfolio Optimization (AI)

Location: Hybrid work in the New York City or Boston metro areas, with 3 days per week in the office. Applicants in the Research Triangle, NC, and San Francisco Bay Area may also be considered. Applicants must reside in these locations or be willing to relocate to the United States.

Total compensation: $154,500–$202,000 per year, plus equity, comprehensive benefits, and perks.

Company

hirify.global is a technology and AI-driven pharmaceutical company developing platforms and capabilities to accelerate drug development and clinical trials.

What you will do

• Implement and maintain the portfolio engine, including order management, execution simulation, portfolio construction, and performance tracking.
• Design risk frameworks for portfolios of drug development programs with different risk profiles, timelines, and failure modes.
• Run temporally constrained backtesting experiments to evaluate strategies and measure the value of new evidence sources.
• Integrate clinical trial data, genomic evidence, real-world data, and proprietary tools into portfolio analytics pipelines.
• Build dashboards and reporting with product and engineering teams to communicate portfolio performance, risk metrics, and strategy comparisons.
• Feed portfolio-level evaluation back into model improvement and evidence prioritization.

Requirements

• PhD in statistics, finance, physics, computational science, engineering, or a related quantitative field.
• 1–3 years of experience in quantitative research, data science, or analytics in life sciences or an adjacent field; substantive internships qualify.
• Strong Python skills and experience with data-intensive workflows using pandas, NumPy, and SciPy.
• Understanding of portfolio construction and risk concepts, including position sizing, rebalancing, Sharpe ratio, drawdown, volatility, and benchmark comparison.
• Experience working with messy real-world datasets, including data wrangling, deduplication, and quality assessment.
• Ability to present quantitative results clearly to technical and business stakeholders.

Nice to have

• Experience with backtesting frameworks or portfolio simulation, such as vectorbt, Backtrader, or custom implementations.
• Exposure to healthcare, pharmaceutical, or biotech data, including clinical trials, claims, omics, or real-world evidence.
• Experience with alternative data, probability-of-success modeling, drug development decision analysis, or health economics.
• Experience with LLMs or AI/ML pipelines.
• Familiarity with Streamlit, Plotly, Dash, Dagster, or Airflow.

Culture & Benefits

• Work at the intersection of quantitative finance, healthcare data, and AI-driven drug development.
• Contribute to accelerating the development of new medicines.
• Hybrid work model in key U.S. hiring hubs.
• Equity, comprehensive benefits, and generous perks.