Brahma is conducting this search on behalf of our client.
We're hiring across quantitative and machine learning roles and looking for strong talent at the intersection of rigorous analysis and applied impact. Whether your background skews toward research, engineering, or analytics, if you think in numbers and build with data, we want to talk.
These are not cookie-cutter roles. You'll work on high-ambiguity problems, collaborate across disciplines, and be expected to go from exploratory analysis all the way through to deployed solutions.
What You'll Do
- Design and build machine learning models, statistical methods, and quantitative frameworks
- Work with large, complex datasets to surface actionable signals
- Run experiments, measure outcomes, and iterate rigorously
- Partner with engineering, product, and business stakeholders to drive decisions
- Communicate findings clearly to technical and non-technical audiences alike
What We're Looking For
- Advanced degree (MS or PhD) in a quantitative discipline — statistics, mathematics, CS, physics, operations research, or equivalent experience
- Strong foundations in probability, statistics, linear algebra, and optimization
- Proficiency in Python and relevant ML libraries (PyTorch, TensorFlow, scikit-learn, etc.)
- Experience with SQL and large-scale data
- Ability to move fluidly between ambiguous research and production-quality work
Nice to Have
- Domain experience in [NLP / computer vision / time series / causal inference / reinforcement learning]
- MLOps and cloud platform experience (AWS, GCP, Azure)
- Published research, open-source contributions, or competition track record
