Staff Data Scientist

Clutch Canada; Toronto; On-site; CAD 100,000 - 130,000; Full time

Job summary

A leading online used car retailer is seeking a Staff Data Scientist to enhance pricing algorithms through machine learning. This high-ownership role involves improving existing ML systems, analyzing market data, and collaborating across teams. Requirements include 8+ years experience, Python proficiency, and the ability to operate in ambiguous environments. The candidate will drive measurable business impact and contribute to various ML domains including fraud detection and recommendations.

Qualifications

• 8+ years of experience as a Data Scientist delivering measurable impact.
• Proven ability to navigate ambiguity and own strategic roadmaps.
• Deep proficiency in Python for full project lifecycle management.

Responsibilities

• Own improvements to pricing algorithms for margin and conversion.
• Analyze market data to enhance vehicle pricing strategies.
• Build and deploy ML models into production for revenue impact.

Skills

Machine Learning

Python

Statistical Analysis

Data Engineering

Communication

Job description

About Clutch:

Clutch is Canada’s largest online used car retailer, delivering a seamless, hassle-free car-buying experience to drivers everywhere. Customers can browse hundreds of cars from the comfort of their home, get the right one delivered to their door, and enjoy peace of mind with our 10-Day Money-Back Guarantee… and that’s just the beginning.

Named one of Canada’s top growing Companies two years in a row and also awarded a spot on LinkedIn’s Top Canadian Startups list, we’re looking to add curious, hard-working, and driven individuals to our growing team.

Headquartered in Toronto, Clutch was founded in 2017. Clutch is backed by a number of world-class investors, including Canaan, BrandProject, Real Ventures, D1 Capital, and Upper90. To learn more, visit clutch.ca.

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.