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Staff Data Scientist

Clutch Technologies Inc.

Toronto

On-site

CAD 100,000 - 140,000

Full time

2 days ago
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Job summary

A leading technology firm in Toronto is seeking a Staff Data Scientist to enhance pricing algorithms and machine learning systems. This role is pivotal, requiring over 8 years of experience in data science with proven results. The candidate will tackle real-world ML applications impacting business outcomes and will need to effectively communicate technical details to stakeholders. Opportunities to advance into additional machine learning areas exist within this data-driven team.

Qualifications

  • 8+ years of experience as a Data Scientist with measurable business impact.
  • Proven ability to navigate high-ambiguity environments.
  • Deep Python proficiency covering the complete data lifecycle.

Responsibilities

  • Drive improvements to pricing algorithms focusing on margin and conversion.
  • Explore market data to identify relationships affecting pricing outcomes.
  • Build and deploy machine learning models in production.

Skills

Machine Learning
Python
Data Analysis
Statistical Analysis
Communication
Job description
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:
  • 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.
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