Data Scientist, Programmatic Algorithms com is the world’s leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products
- Performance (affiliate), Creator (influencer), and Advocate (customer referral)
- unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. We're seeking a Senior Data Scientist to serve as an embedded Data Scientist within our Programmatic Experience Group. You'll own the design and deployment of machine learning models that optimize yield, pricing, and inventory allocation at scale — sitting at the intersection of data science, platform engineering, and marketplace economics. You'll work end-to-end: architecting the data pipelines that feed your models, engineering the features that drive performance, and deploying real-time inference systems that make decisions at speed. Your work directly determines how effectively Impact's programmatic marketplace balances advertiser performance with publisher monetization — making this one of the highest-leverage technical roles in the business. You'll collaborate closely with Product, Data Science, and Programmatic Delivery Engine Engineering, but you operate with significant autonomy. You're expected to bring both the modeling rigor of a data scientist and the production instincts of an ML engineer — and to be genuinely excited about both.



Design and deploy ML models that optimize auction pricing, bid shading, floor price setting, and yield across Impact's programmatic inventory. Build and iterate on real-time pricing algorithms that balance short-term revenue efficiency with long-term publisher and advertiser health. Develop and maintain feedback loops that allow pricing models to adapt to shifting market conditions, inventory mix, and demand patterns. Inventory Allocation & Supply Optimization Own ML-driven inventory allocation logic: routing, pacing, and matching supply to demand across partner segments, deal types, and campaign objectives. Build models that forecast inventory availability, demand curves, and clearing prices to support proactive allocation decisions. Identify and address inefficiencies in inventory utilization — including unsold inventory, suboptimal deal matching, and allocation imbalances across the publisher base. Data Architecture & Feature Engineering Design and own the data infrastructure that feeds programmatic models: event pipelines, feature stores, training datasets, and real-time feature serving. Engineer high-signal features from auction logs, bid stream data, user signals, contextual attributes, and historical performance — at the scale of programmatic data volumes. Build robust data pipelines with production-grade standards: reliability, observability, versioning, and effective reprocessing.



Real-Time Inference & Production ML Build monitoring systems that track model performance, data drift, and system health in production; Own the full model lifecycle: training, evaluation, deployment, A/B testing, and iteration. Experimentation & Performance Measurement Design and execute rigorous A/B and holdout experiments to measure the causal impact of model changes on yield, fill rate, advertiser performance, and publisher revenue. Research and implement adaptive, self-learning components within the programmatic stack — including contextual bandits, reinforcement learning signals, and online learning approaches where appropriate. Stay current with advances in programmatic ML, auction theory, and online optimization; Serve as the primary ML technical partner for the Rubicon product and engineering teams; translate business requirements into modeling approaches and communicate technical tradeoffs clearly. Collaborate with Data Science peers on shared infrastructure, modeling standards, and cross-domain feature reuse.

Experience:
5+ years in data science, ML engineering, or quantitative research, with at least 2+ years building and deploying ML models in programmatic advertising, ad tech, marketplace optimization, or a closely related domain (e.g., Demonstrated understanding of programmatic auction mechanics (RTB, header bidding, floor pricing, deal types, bid shading) and how ML can be applied to optimize outcomes across the supply-demand stack. Production ML engineering: Proven ability to take models from prototype to production independently — including real-time inference, monitoring, retraining pipelines, and SLO ownership. Data architecture: Experience designing and building data pip

📌 DATA SCIENTIST MARKETING SENIOR (Canada)
🏢 Impact
📍 Canada