Staff Data Scientist – Ads Identity & Lift Expert in Toronto

Staff Data Scientist – Ads Identity & Lift Expert in Toronto

Toronto Full-Time 121500 - 148500 $ / year (est.) On-site
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At a Glance

  • Tasks: Design identity resolution models and lead large-scale experimentation.
  • Company: Join Reddit, a leading platform known for its innovative culture.
  • Benefits: Attractive salary, flexible work options, and opportunities for professional growth.
  • Other info: Collaborative environment with a focus on cross-functional leadership.
  • Why this job: Make a significant impact on advertising strategies and drive measurable results.
  • Qualifications: Advanced degree in data science with 6-10+ years of experience required.

The predicted salary is between 121500 - 148500 $ per year.

Reddit, Inc. is seeking a senior data/ML scientist to design identity resolution models for on-/off-platform actions and lead rigorous experimentation at scale.

You will shape signal sources, build evaluation frameworks, and collaborate with engineering, product, and sales to align strategic goals and drive measurable ROAS.

The role requires advanced degrees and 6–10+ years in data science with strong Python/R and SQL skills, plus experience in A/B testing and cross-functional leadership.

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Staff Data Scientist – Ads Identity & Lift Expert in Toronto employer: Reddit, Inc.

Reddit is an exceptional employer that fosters a culture of innovation and collaboration, particularly in the vibrant city of Toronto. With a strong emphasis on employee growth, you will have the opportunity to lead impactful projects in Ads Measurement while working alongside talented professionals across various disciplines. The company values work-life balance and offers competitive benefits, making it a rewarding place for those looking to make a meaningful contribution in the tech industry.

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Contact Details:

Reddit, Inc. Recruitment Team

We think you need these skills to ace Staff Data Scientist – Ads Identity & Lift Expert in Toronto

Identity Resolution Models
Experimentation at Scale
Signal Sources Shaping
Evaluation Frameworks
Collaboration with Engineering
Collaboration with Product
Collaboration with Sales