Senior Data Scientist II - Ads Optimization

Accountabilities:

• Own the analytics and experimentation strategy for pacing, targeting, budget allocation, and related advertising optimization areas, driving measurable improvements in advertiser outcomes and platform efficiency.
• Design and develop intelligent pacing algorithms and budget allocation systems using adaptive control, model-predictive control, and related optimization techniques for high-throughput production environments.
• Lead end-to-end experimentation across optimization systems, including experiment design, measurement, signal diagnosis, interpretation, and translation of results into actionable recommendations.
• Apply causal inference, statistical modeling, and machine learning techniques to optimize how advertising spend is distributed across time and auction opportunities.
• Balance advertiser objectives, user experience, platform efficiency, and revenue considerations when developing and evaluating optimization strategies.
• Partner closely with Product, Engineering, Machine Learning, Data Science, and Analytics teams to take concepts from problem framing and research through production deployment, measurement, and iteration.
• Present complex analytical findings, experiment results, and strategic recommendations clearly to senior Product, Engineering, and Data Science stakeholders.
• Act as a technical anchor and thought leader within the Ads Optimization space, helping establish analytical standards and mentoring other data scientists and team members.
• Collaborate with Ads Product and Sales teams to gather advertiser feedback, understand business needs, and translate market insights into optimization priorities.

Requirements:

• 6+ years of professional experience in data science or a related quantitative field, with proven experience working on advertising optimization at an ad technology company or comparable environment.
• Hands-on experience with advertising systems such as pacing, targeting, bidding, budget allocation, or related optimization mechanisms.
• Strong foundation in product and data analytics, A/B experimentation, causal inference, statistical modeling, and quantitative problem solving.
• Demonstrated experience shipping optimization systems into production, including measuring their performance and iterating based on real-world results rather than focusing solely on research.
• Strong understanding of how experimentation and statistical analysis can inform decisions in complex, high-scale production environments.
• Excellent communication skills, with the ability to synthesize sophisticated technical findings and explain their business implications to senior technical and non-technical stakeholders.
• Strong cross-functional collaboration skills and the ability to influence product and engineering priorities through rigorous analysis and evidence.
• Experience working directly with advertising product teams, sales teams, or advertisers to shape product and algorithmic priorities is a plus.
• Background in budget-constrained allocation, adaptive control, model-predictive control, or related optimization methods in production systems is desirable.
• Ability to operate independently on ambiguous problems while maintaining strong analytical rigor, business judgment, and attention to measurable outcomes.

Benefits:

• Canadian base salary of $192,000–$202,500 CAD for eligible candidates.
• Eligibility for a new-hire equity grant and annual equity refresh grants.
• Market-competitive compensation and benefits based on work location.
• Remote-first flexibility, with the position currently available only to candidates permanently located in Ontario, Alberta, British Columbia, or Nova Scotia.
• Comprehensive benefits designed to support employees across financial, health, and overall wellbeing needs.
• Opportunity to work on large-scale advertising optimization systems with direct impact on advertiser outcomes and platform performance.
• Collaboration with Product, Engineering, Machine Learning, Data Science, and Sales teams in a highly cross-functional environment.
• Opportunity to influence experimentation strategy, optimization algorithms, and technical standards while mentoring peers.
• Flexibility to work from home, an office, or another suitable location under a Flex First working model, with opportunities for regular in-person connection and team events.