Telecommut
Data Scientist (Bayesian Modeling & Causal Inference)
Location: New York
Overview
This team is looking for a Data Scientist with strong expertise in Bayesian methods, probabilistic modeling, and causal inference to join a financial markets content team. This role blends quantitative research, client-facing analytics, and product development.
You'll build models that extract insight from sparse, noisy, and fragmented datasets, helping clients make more informed investment decisions. Working closely with strategists, sales, and market specialists, you'll develop frameworks that surface inconsistencies across macro indicators, market pricing, company fundamentals, and alternative data. Your work will directly power client deliverables, investment views, and new analytical products.
This is an opportunity for someone who enjoys hands-on modeling, applied research, and using advanced statistics to solve real-world market problems.
Responsibilities
- Develop Bayesian and probabilistic models to generate insight from incomplete or noisy data
- Build frameworks that quantify uncertainty and inform investment decisions
- Design network / graph-based models to capture relationships across diverse datasets
- Partner with client-facing teams to translate quantitative outputs into clear, actionable insights
- Create tools and applications that scale the use of probabilistic modeling across the business
- Apply causal inference methods to better understand market drivers
- Communicate complex statistical concepts to both technical and non-technical audiences
Qualifications
- 3-7 years in Data Science, Quant Research, Applied Statistics, or similar
- Advanced degree (PhD preferred) in a quantitative field (e.g., Statistics, CS, Math, Econ)
- Strong background in Bayesian statistics and probabilistic modeling
- Experience applying advanced methods in real-world settings
- Proficiency in Python or R (including practical use of MCMC or similar techniques)
- Experience working with large, complex datasets
- Strong communication skills with client-facing experience
Preferred
- Experience with real-time or online prediction systems
- Familiarity with causal inference and experimental design
- Experience with graph or network models
- Background building client-facing tools or analytics products
