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.