Multiple Openings: Data Science & Data Engineering talent

Our client is a global institutional investor managing capital at scale across public and private markets. The mandate for this team is simple: turn data into edge, and edge into decisions. These are not back\-office support roles. You will sit inside the investment and research workflow. Your code, models, and pipelines will touch live capital allocation, risk, and alpha generation. The bar is high because the data is proprietary and the users are portfolio managers, not analysts. We are hiring across three tracks. You can apply to one or multiple if your profile spans them. **Note:** All roles are Individual Contributor positions. Our client is hiring across multiple levels — from strong mid\-level to Principal/Staff **1\. Data Scientist – Investment Research** Mission Build and deploy models that extract signal from structured and unstructured data to inform discretionary and systematic investment decisions. Scope * Partner directly with sector PMs and researchers to frame ambiguous problems as testable hypotheses * Develop ML/NLP/time\-series models on alternative data, market data, and fundamental datasets * Productionize research: your notebook isn’t the deliverable — the monitored, versioned model is * Explain trade\-offs to non\-technical stakeholders. “The AUC is 0\.82” is not an answer. Profile We’re Calibrating For * Experience quant research, data science, or ML engineering with direct exposure to financial markets * Strong Python \+ SQL. Experience with scientific stack: pandas, scikit\-learn, PyTorch/TensorFlow, statsmodels * Comfort with messy, real\-world data. You’ve joined 12 tables to build one feature and lived to tell about it * MSc/PhD in STEM, Stats, CS, Financial Engineering preferred, but we’ll take a killer GitHub over a logo * You think in base rates and Bayesian updates, not just in accuracy scores **2\. Data Engineer – Investment Platform** Mission Design and run the data backbone that feeds research, backtesting, and production trading. Latency, lineage, and correctness are your P\&L. Scope * Build and maintain high\-throughput pipelines for market, reference, alternative, and fundamental data * Architect storage \+ compute: think petabyte\-scale, cloud\-native, but cost\-aware * Implement data quality, observability, and entitlement frameworks. Bad data here loses real money * Enable quants and DS to self\-serve without creating a ticket or waiting 3 days Profile We’re Calibrating For * 4\-10 years building production data systems, ideally in finance, trading, or high\-stakes analytics * Python/Scala/Java \+ SQL. Spark, Airflow, Kafka, dbt, Snowflake/BigQuery/Databricks * Deep understanding of partitioning, versioning, and replay. “It works on my laptop” is a fireable offense * You design for failure. Your pipelines have DLQs, retries, and alerts that actually wake someone up * Bonus: experience with market data, tick data, point\-in\-time correctness, corporate actions **3\. Quantitative Developer – Systematic Strategies** Mission Sit between research and production. Take prototype strategies and make them run fast, clean, and safely with real capital. Scope * Translate researcher code into low\-latency, testable, production\-grade systems — C\+\+/Python/Rust * Build sim/backtest frameworks that match production behavior. No “research vs prod” drift * Own tooling for signal generation, portfolio construction, risk checks, and execution hooks * Profile, optimize, and harden. When the market opens, your code doesn’t get a second chance Profile We’re Calibrating For * 3\-10 years as a quant dev, low\-latency dev, or SWE in trading/systematic funds * Strong C\+\+ or Python with performance intuition. You know why virtual functions hurt and when to use them anyway * Linux, git, CI/CD, numerical computing. You’ve debugged a race condition at 2am before a US open * Undergrad/MS in CS, Math, Physics, Engineering. Competitive programming/ICPC background is a plus * You respect researchers but you don’t ship their for\-loops to prod **Shared DNA Across All Three Roles** Our client hires for three traits regardless of track: 1. Ownership – You ship, you support, you fix. No “not my system” here. 2. Rigor – Financial data has no undo button. Test it, document it, prove it. 3. Low ego, high output – The PM is your customer. Your job is to make them better, not to publish papers. Hiring Process Typically 3 rounds: Technical deep\-dive → System/research design → CIO/Head of Platform. We move in days, not months. Take\-home tests are rare; live problem solving is common.