The role, as published
About the company
Our client is a fast-growing financial technology company.
The role
Raydar is recruiting for this role on behalf of our client. Lead applied research on predictive models, dividing your time between inventing new modeling approaches and moving the strongest ones into live use. You will collaborate closely with senior technical and business leaders in an on-site team.
What you'll do
- Devote roughly 70% of your time to research, inventing and testing new model designs for risk prediction.
- Mine sizable, imperfect in-house data to surface new features and predictive signals.
- Investigate language-model and multimodal techniques that go beyond current tree-based approaches.
- Devote roughly 30% of your time to production work, covering deployment, data sourcing needs and ongoing monitoring.
- Work with a highly technical team and report to senior business and engineering leaders.
What we're looking for
- Roughly 5 to 10 years in applied machine learning research, where academic lab and doctoral work can count toward that time.
- Hands-on background in an academic or university research lab.
- An advanced degree in a quantitative discipline such as computer science, mathematics or physics, ideally from a highly ranked university.
- Deep practical experience building predictive models with classical machine learning methods; a profile centered only on language models is not a fit.
- A record of modeling complex, noisy real-world systems where many factors interact, such as robotics, physics or chemistry; finance experience is not needed.
- Strong Python skills for both analysis and production machine learning.
- Disciplined approach to testing models, including time-aware validation, probability calibration, decision threshold tuning, spotting data leakage and judging statistical significance.
- Working knowledge of SQL, a dataframe library such as Pandas, and model explainability tooling.
- Ability to work on-site five days a week.
Bonus points
- Experience at leading AI research organizations.
- Exposure to large language models.
Location and work model
- San Francisco, CA, United States
- On-site, 5 days per week in office
- Full-time
Compensation, as the posting describes it
- Equity