About the role:Clutch is hiring a Staff Data Scientist to lead major improvements to our pricing algorithms and applied machine learning systems.
This is a high-ownership role for someone who thrives in ambiguity, can go deep on research and modeling, and has a track record of deploying ML to production with measurable business impact. You'll work on ML systems that already drive real outcomes - including pricing models that purchase >$1M of vehicles per day with no human intervention - with significant opportunity to take them to the next level as we scale.
You'll join a small, high-leverage data team where your work will be visible, measurable, and business-critical, with the chance to expand into additional high-impact ML domains like lending, logistics optimization, fraud detection, and recommendations. In this role, you'll own problem areas end-to-end from identifying opportunities and shaping the approach, to shipping production models and driving measurable improvements in margin and conversion.
What you'll do:- Own and drive improvements to Clutch's pricing algorithms, balancing margin, conversion, and customer experience.
- Deep-dive into market and vehicle data to identify the key relationships between vehicle attributes, market dynamics, and pricing outcomes.
- Build, validate, and deploy ML models and algorithms into production - and iterate quickly based on real-world performance.
- Lead feature engineering, model evaluation, and experimentation design.
- Partner with Product, Engineering, Strategy & Ops, Sell-To-Clutch & Retail to prioritize the highest-impact opportunities.
- Contribute to additional applied ML domains as needed, including:
- Financing / lending decisioning
- Fraud detection
- Search and discovery optimization
- Vehicle recommendations / personalization
What we're looking for:- 8+ Years of Experience: A proven track record as a Data Scientist, with a history of delivering measurable business impact through machine learning.
- 0-to-1 Strategic Autonomy: Proven ability to navigate high-ambiguity environments. You own the roadmap by evaluating the data, identifying untapped opportunities, and formulating your own research theories.
- End-to-End Technical Ownership: Deep Python proficiency with the ability to own the entire lifecycle: from raw data exploration and feature engineering to model architecture and production deployment.
- Production-Grade ML: Strong experience building and deploying traditional ML algorithms into live environments, ensuring they are robust, scalable, and maintainable.
- Foundational Rigor: Strong statistical fundamentals and a disciplined approach to validation. You ensure that every model is built on a foundation of sound logic and clean data, maintaining high standards for accuracy and reliability without external oversight.
- Excellent Communication: Able to bridge the gap between complex technical findings and business ROI. You can distill "black box" complexity into clear trade-offs and actionable recommendations for business leaders.
