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Sayash RaajRésumé

Experience

Quantitative work in industry

Three years across Fidelity Investments and BlackRock: data scientist, then Lead in Quant Strategy within 14 months, then quantitative portfolio management in BlackRock Index Equity.

BlackRock

Index Equity · Mumbai, India

  1. Quantitative Portfolio Management, Index Equity

    Aug 2026 – Present

    • Partner with Index Equity portfolio managers to translate investment methodology and constraints into Aladdin portfolio-management workflows.
    • Led Munich ETF onboarding, extending the existing US ETF order-generation workflow with fund-specific basket construction and constraint checks.
    • Apply constraint-based portfolio logic to identify benchmark-tracking deviations and formulate proposed orders for PM review.

    Related work Index ETF portfolio management

Fidelity Investments

Bengaluru, India

  1. Lead, Financial Solutions, Quant StrategyPromoted

    Oct 2024 – Aug 2026

    • Promoted to Lead within 14 months, the fastest promotion in the Financial Solutions Quant Strategy group; led a four-person quant team from research through production.
    • Designed a multi-account, tax-aware asset-location optimizer using convex optimization and stochastic simulation (Python, Gurobi), reducing annualized tax leakage by 12 bps/yr, validated across 30-year Monte Carlo simulations.
    • Built a pre-Medicare healthcare cost model on Snowflake data with 10,000-scenario Monte Carlo simulation, reducing expected policy costs by 32% for target cohorts in scenario-design experiments.
    • Won two firmwide innovation competitions at Fidelity, leading the team each time.

    Related work Tax-aware multi-account asset location; Pre-Medicare healthcare cost simulation

  2. Data Scientist, Research & Data

    Jul 2023 – Oct 2024

    • Sole author of Context-Masked Meta-Prompting for Privacy-Preserving LLM Adaptation in Finance, accepted at a NeurIPS 2025 workshop.
    • Developed a synthetic financial data generator: a conditional deep learning model with post-hoc constraint enforcement. 95.16% statistical fidelity, 3× generation throughput, 40% lower per-dataset operational cost.
    • Automated the detection of deterministic constraints across 1.2M+ records, reducing manual constraint-labeling time by 94%.
    • Ran non-parametric statistical analysis (Kolmogorov–Smirnov tests, t-tests, bootstrap) quantifying ESG rating divergence across mutual funds and ETFs.

    Related work Constrained synthetic financial data; Automated constraint discovery; ESG rating divergence