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.
Requirements
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.
Benefits
Compensation and benefits
- Base salary: USD 250,000 to 400,000 per year
- Equity
Location and work model
- San Francisco, CA, United States
- On-site, 5 days per week in office
- Full-time
Skills Required
- 5 to 10 years of applied machine learning research experience, including academic lab or doctoral work
- Hands-on experience in an academic or university research lab
- Advanced degree in a quantitative discipline such as computer science, mathematics, or physics
- Practical experience building predictive models with classical machine learning methods
- Experience modeling complex, noisy real-world systems with interacting factors
- Strong Python skills for analysis and production machine learning
- Experience with time-aware validation, probability calibration, decision threshold tuning, data leakage detection, and statistical significance evaluation
- Working knowledge of SQL
- Working knowledge of a dataframe library such as Pandas
- Working knowledge of model explainability tooling
- Ability to work on-site five days per week
- Experience at leading AI research organizations
- Exposure to large language models
What We Do
Raydar is a talent acquisition and business consulting firm that connects world-class and emerging talent with growing organizations. It supports companies through team development, strategic hiring, and customized growth solutions, helping clients recruit roles such as engineers, product managers, executives, legal counsel, and quantitative traders. Raydar focuses on understanding each organization’s needs, culture, and long-term goals to build high-impact teams.
