Sr. Data Scientist, Programmatic Algorithms

Impact.com•Seattle, WA

1d•Hybrid

About The Position

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. 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. 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. 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. 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. 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.

Requirements

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).
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.
Proven ability to take models from prototype to production independently — including real-time inference, monitoring, retraining pipelines, and SLO ownership.
Experience designing and building data pipelines, feature stores, and training infrastructure for high-volume, low-latency ML systems.
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.
Experience processing and modeling at programmatic data scale: high-cardinality auction logs, bid stream data, impression and click events.
Strong grasp of causal inference and experiment design in online, delayed-feedback environments (auction holdouts, switchback tests, variance reduction techniques).
Ability to explain complex modeling decisions and tradeoffs to Product and business stakeholders; comfortable presenting in cross-functional forums.
Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, Economics, or similar); Master's/PhD preferred.

Nice To Haves

Direct 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.
Familiarity with privacy-preserving ML techniques relevant to programmatic (differential privacy, federated learning, cookieless attribution modeling).
Experience with GCP tools (BigQuery, Vertex AI, Dataflow, Pub/Sub) and/or Databricks/Spark for large-scale event processing and model training.
Exposure to supply forecasting, inventory management, or capacity planning in programmatic or marketplace contexts.
Familiarity with Impact's affiliate and partnership ecosystem, or prior experience at the intersection of performance marketing and programmatic delivery.

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.
Quantify the revenue impact of pricing model improvements; communicate tradeoffs between yield maximization, fill rate, and partner ROI to stakeholders.
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 requirements for auction-time decision-making.
Build monitoring systems that track model performance, data drift, and system health in production; define alerting thresholds and retraining triggers.
Partner with MLOps and Platform Engineering to ensure scalable, low-latency serving infrastructure meets SLOs under high-volume auction traffic.
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.
Translate experimental results into clear business narratives; present findings and recommendations to Product and business stakeholders.
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.
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 requirements into modeling approaches and communicate technical tradeoffs clearly.
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.

Benefits

Medical, Dental, and Vision insurance
Office-only catered lunch every Thursday, a healthy snack bar, and great coffee to keep you fueled
Flexible spending accounts and 401(k)
Responsible PTO policy
Mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year, with additional dependent coverage.
Monthly gym reimbursement policy
Restricted Stock Units (RSUs)
Free Coursera subscription
PXA courses
26 weeks of fully paid leave for the primary caregiver
13 weeks fully paid leave for the secondary caregiver
Technology stipend to help you set up your home office
Monthly allowance to cover your internet expenses