data
Posted Apr 27Sr. Data Scientist, Programmatic Algorithms
Vancouver, CanadaOn-site
Responsibilities
- 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.
- 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.
- 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 efficient reprocessing.
- Deploy models to production real-time inference environments; own latency, reliability, and throughput
- Build monitoring systems that track model performance, data drift, and system health in production; define alerting thresholds and retraining triggers.
- Own the full model lifecycle: training, evaluation, deployment, A/B testing, and iteration.
- 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.
- Build evaluation frameworks that go beyond offline metrics — validating model behavior in live auction environments where feedback signals are delayed or noisy.
- Research and implement adaptive, self-learning components within the programmatic stack — including contextual bandits, reinforcement learning signals, and online learning approaches where appropriate.
- Design feedback mechanisms that close the loop between auction outcomes, model updates, and system behavior; reduce reliance on manual tuning and rule-based overrides.
- Collaborate with Data Science peers on shared infrastructure, modeling standards, and cross-domain feature reuse.
- Document models, architectures, and experimental findings to a standard that enables review, replication, and knowledge transfer across teams. What You Bring: Required •
Requirements
- 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'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. What You'll Do:
- Real-Time Inference & Production ML
- Stay current with advances in programmatic ML, auction theory, and online optimization; evaluate applicability to Impact's specific marketplace dynamics.
- Serve as the primary ML technical partner for the Rubicon product and engineering teams; translate business
- 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., real-time bidding, dynamic pricing, auction systems).
- Programmatic & marketplace depth: 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 pipelines, feature stores, and training infrastructure for high-volume, low-latency ML systems. Technical skills:
- Strong Python and SQL; proficiency with ML libraries (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow) and large-scale data tools (Spark, Kafka, or equivalent streaming/batch frameworks). •
- Experience with real-time feature serving and low-latency model deployment (REST APIs, gRPC, or streaming inference).
- Familiarity with production ML workflows: model versioning, drift monitoring, A/B testing, evaluation, and retraining. •
- Experimentation rigor: Strong grasp of causal inference and experiment design in online, delayed-feedback environments (auction holdouts, switchback tests, variance reduction techniques).
- Communication: Ability to explain complex modeling decisions and tradeoffs to Product and business stakeholders; comfortable presenting in cross-functional forums.
- Education: Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, Economics, or similar); Master's/PhD preferred.
- experience with SSP, DSP, or exchange-side yield optimization — particularly floor price optimization, bid landscape modeling, or deal matching algorithms.
- Familiarity with auction theory (first-price vs. second-price dynamics, optimal reserve pricing, revenue equivalence) and its practical implications for programmatic ML. •
- Experience with contextual bandits, multi-armed bandits, or reinforcement learning applied to real-time decisioning problems.
- Knowledge of online learning and adaptive algorithms in production environments with non-stationary data distributions. •
- Experience with GCP tools (BigQuery, Vertex AI, Dataflow, Pub/Sub) and/or Databricks/Spark for large-scale event processing and model training.
- Familiarity with Impact's affiliate and partnership ecosystem, or prior
- experience at the intersection of performance marketing and programmatic delivery. What Sets You Apart
- You understand programmatic auctions not just as an engineer but as an economist — you think about incentive structures, equilibrium dynamics, and how model decisions ripple through the supply-demand stack.
- Full-stack ML ownership. You're as comfortable designing a feature store schema as you are tuning a gradient boosting model or debugging a latency spike in production. You own the whole chain.
- You know how to run clean experiments in environments where feedback is delayed, data is noisy, and business pressures create tradeoffs.
- experience of the applicant along with the
Benefits
- Salary Range: $165,000 - $185,000 per year, plus an additional 5% variable annual bonus contingent on Company performance and eligible to receive a Restricted Stock Unit (RSU) grant.
- *This is the pay range the Company believes is equitable for this position at the time of this posting.
- Consistent with applicable law, compensation will be determined based on the skills, qualifications, and
- requirements of the position, and the Company reserves the right to modify this pay range at any time.
- Benefits and Perks:
- benefits package that supports your well-being, growth, and work-life balance.
- benefits: Health & Prescription coverage, vision and dental care, virtual health care, out-of-country medical coverage
- Insurance coverage (life, short-term disability, long-term disability, and more)
- Health Care Spending Account
- Flexible Working: Our Responsible PTO policy means you can take the time off you need to rest and recharge.
- Our mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year, with additional dependent coverage.
- A Stake in Our Growth: We offer Restricted Stock Units (RSUs) as part of our total compensation, giving you a stake in the company's growth with a 3-year vesting schedule, pending Board approval.
- Parental Support: We offer a generous parental leave policy, 26 weeks of fully paid leave for the primary caregiver and 13 weeks fully paid leave for the secondary caregiver.
- Technology Financial Support: We provide a technology stipend to help you set up your home office and a monthly allowance to cover your internet expenses. Note:
Contact
- impact.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.
- As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most.
- Today, over 5,000 global brands - including Walmart, Uber, Shopify, Lenovo, L’Oréal, and Fanatics - rely on impact.com to power more than 350,000 partnerships that deliver measurable business results.
- Your Role at impact.com :
- At impact.com, we believe that when you’re happy and fulfilled, you do your best work. That’s why we’ve built a
- impact.com is proud to be an equal-opportunity workplace.
Additional details
- Its award-winning products - Performance (affiliate), Creator (influencer), and Advocate (customer referral) - unify every type of partner into one integrated platform.
- We're seeking a Senior Data Scientist to serve as an embedded Data Scientist within our Programmatic Experience Group.
- This is a high-craft, high-ownership individual contributor role.
- 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.
- Quantify the revenue impact of pricing model improvements; communicate tradeoffs between yield maximization, fill rate, and partner ROI to stakeholders.
- Partner with MLOps and Platform Engineering to ensure scalable, low-latency serving infrastructure meets SLOs under high-volume auction traffic.
- Translate experimental results into clear business narratives; present findings and recommendations to Product and business stakeholders.
- requirements into modeling approaches and communicate technical tradeoffs clearly.