Axial Search recruits for AI transformation roles across North America, building long-term relationships with leaders across every industry vertical. Apply today to express your interest in this position and others like it. Visit our website to learn more about our process and to find free tools to support your job search — including our live job market dashboard with salary, skills, and hiring-trend data from thousands of AI transformation roles.

What the market looks like

New York's buy-side and sell-side firms are the single largest concentration of quantitative and ML-focused data science roles we track — and the hiring bar is notoriously selective. Compensation at senior-IC level is typically packaged as base plus bonus plus carry, with base alone generally in a $165K–$280K range and all-in compensation significantly higher at the top firms. The strongest quantitative data scientists we place bring deep time-series modeling experience, the discipline to validate carefully, and the ability to go from research notebook to production-grade code.

Job responsibilities

Research, build, and deploy ML models for signal generation, risk, or execution
Own the full research lifecycle — data, feature engineering, modeling, backtesting, and deployment
Partner with quantitative researchers, engineers, and trading or portfolio teams
Contribute to the firm's core research infrastructure and shared tooling
Continuously monitor live models and refine as market regimes shift
Communicate research findings clearly to senior stakeholders, including portfolio managers and risk committees
Challenge your own results ruthlessly — backtest quality matters more than headline performance
Collaborate with non-quant engineers on low-latency production integration where required

Candidate requirements

4+ years in quantitative research, quantitative development, or applied ML in financial services
Advanced degree (MS or PhD) in a quantitative field, or equivalent applied experience
Strong Python and production-grade code practices
Deep statistical and ML foundation — particularly time-series and non-stationary data
Experience with backtesting, simulation, and careful model validation
Strong written and verbal communication

Quantitative Data Scientist (ML)