Senior Quantitative Developer / Data Scientist – Enterprise Data Technology
Balyasny Asset ManagementSenior Quantitative Developer / Data Scientist – Enterprise Data Technology
Posted 237 Days Ago
With respect to NY, CA, and IL based applicants, the starting base pay range for this role is between USD 175000 and USD 250000 annually. The actual base pay is dependent upon several factors, including, but not limited to, relevant experience, business needs and market demands. This role may also be eligible for bonus compensation and employee benefits.
About the Team
Bam’s Enterprise Data Technology team is responsible for building and maintaining Balyasny's core research and reference data platforms, with a heavy focus on building cross-asset data quality infrastructure. We are a central technology function that directly enables investment decision-making across the firm's strategies.
The Role
We are seeking a Senior Quantitative Developer / Data Scientist to design, build, and enhance mathematical models that validate, monitor, and improve the quality of our core data assets. This is a high-impact role at the intersection of quantitative research, data engineering, and platform development, ideal for someone who has operated in environments where data integrity directly influences trading and research outcomes.
What You'll Do
- Develop and implement statistical models and algorithms to detect anomalies, validate data integrity, and expand coverage across asset classes (equities, futures, fixed income, FX, and more)
- Build scalable, production-grade data quality frameworks that serve as the foundation for firm-wide research and trading systems
- Partner with investment teams to understand data requirements and translate them into robust quality metrics and monitoring solutions
- Continuously research and apply best practices in data validation, statistical testing, and quantitative analysis
What We're Looking For
- Senior-level experience (typically 7+ years) in a quantitative development, data science, or research engineering capacity
- Background in a commercial trading or research environment: ideally at a systematic fund, sell-side research desk (e.g., swaps, rates, or equity derivatives at a firm like JP Morgan), or similar organization where data-driven decisions have direct P&L impact
- Strong foundation in statistics and mathematical modeling: you think like a statistician and can design rigorous tests for data quality and consistency
- Experience with equities and/or futures data: including reference data, corporate actions, pricing, and related research workflows
- Systematic or quantitative research experience: you understand how data flows into models and the downstream consequences of data errors
- Proficiency in Python, SQL, and modern data infrastructure; familiarity with time-series analysis, hypothesis testing, and anomaly detection techniques
- Excellent communication skills and the ability to work cross-functionally with technology and investment professionals
Nice to Have
- Experience with cross-asset data platforms or enterprise data management
- Exposure to fixed income, FX, or derivatives data
- Familiarity with data observability, lineage, or metadata management tools