The opportunity
You’re a data scientist with experience in building and deploying machine learning solutions for complex, and sometimes ambiguous, business problems. You have worked end-to-end on the ML model lifecycle, from early data discovery and analysis to deploying and maintaining models in production.
What you'll do
Partner with product and engineering leaders to identify and scope new data: science use-cases within their domains such as Risk & Insurance and Payments
Utilize SQL to query both structured and unstructured datasets relevant to the problems
Prototype, evaluate, and deploy technical solutions including classical ML,: statistical techniques, algorithms, deep learning, and LLMs
Develop and apply AI-assisted approaches to analysis, model exploration, and: deployment to multiply your impact and expand the reach of data science across the organization
Help implement and curate strategic assets for the team such as data assets,: generalized code and tooling, and reusable frameworks
Support technical and code reviews on the team
What they're looking for
- + years of experience as a data scientist or machine learning practitioner,: with a track record of driving business impact through statistical analysis, machine learning, and productionizing models
- Strong expertise in predictive modeling, numerical algorithms, and statistical methods
- Strong communication and data storytelling skills: you can translate complex findings into clear narratives that influence strategy and decisions
- Strong business acumen and the ability to connect analysis to product: strategy, roadmap tradeoffs, and business outcomes
- Fluent in SQL
- Proficient in Python for both model research and development, as well as: writing production grade code for deployment
- Familiarity with a cloud-based data stack (e.g. Snowflake, dbt)
- Demonstrated curiosity about AI tools and emerging technologies, with a track: record of applying them to accelerate analysis, improve rigor, or expand the reach of data science work
