Sr. Data Scientist, Programmatic Algorithms

About impact.com impact.com, the world’s leading partnership management platform, is transforming the way businesses manage and optimize all types of partnerships—including traditional rewards affiliates, influencers, commerce content publishers, B2B, and more. The company’s powerful, purpose-built platform makes it easy for businesses to create, manage, and scale an ecosystem of partnerships with the brands and communities that customers trust to make purchases, get information, and entertain themselves at home, at work, or on the go. To learn more about how impact.com’s technology platform and partnerships marketplace is driving revenue growth for global enterprise brands such as Walmart, Uber, Shopify, Lenovo, L’Oreal and Fanatics, visit www.impact.com.

impact.com

Our partnership management platform automates every type of business partnership, at scale.

Marketing SaaS TechnologyPartner Management SoftwareCall TrackingMulti-Source AttributionMulti-Channel Media TrackingMedia ManagementBuyer Journey InsightsAlgorithmic AttributionAd Fraud DetectionMarTech

I

impact.com

1,001-5,000 employees

Software Development

Santa Barbara, California

2008

Privately Held

97k LinkedIn followers

Raised $361.4M

• 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.
• 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.
• 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.
• 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.

Required

• 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.
• 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.
• high-cardinality auction logs, bid stream data, impression and click
• events.
• 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.

Preferred

• 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.
• Marketplace intuition. 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.
• Feedback loop thinking. You don't just deploy models — you design the systems
• that make them smarter over time. You think about how today's decisions
• become tomorrow's training signal.
• Rigor under real-world constraints. You know how to run clean experiments in
• environments where feedback is delayed, data is noisy, and business pressures
• create tradeoffs. You don't let imperfect conditions become an excuse for
• imprecise thinking.
• Pragmatic delivery. You ship. You balance the perfect with the
• production-ready, iterate fast, and know when an MVP outperforms a six-month
• research project.
• Collaborative depth. You build genuine technical trust with engineering and
• product partners — not just by having good ideas, but by following through,
• communicating clearly, and making the integration easy.
• 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 experience of the applicant
• along with the requirements of the position, and the Company reserves the right
• to modify this pay range at any time.

• At impact.com, we believe that when you’re happy and fulfilled, you do your best
• work. That’s why we’ve built a benefits package that supports your well-being,
• growth, and work-life balance.
• Office-only catered lunch every Thursday, a healthy snack bar, and great
• coffee to keep you fueled
• Flexible spending accounts and 401(k)
• Flexible Working: Our Responsible PTO policy means you can take the time off
• you need to rest and recharge. We're committed to a positive work-life
• balance and provide a flexible environment that allows you to be happy and
• fulfilled in both your career and your personal life.
• Health and Wellness: Your well-being is a priority. Our mental health and
• wellness benefit includes up to 12 fully covered therapy/coaching sessions
• per year, with additional dependent coverage. We also offer a monthly gym
• reimbursement policy to support your physical health.
• 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.
• Investing in Your Growth: We’re committed to your continuous learning. Take
• advantage of our free Coursera subscription and our PXA courses.
• 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.
• impact.com is proud to be an equal-opportunity workplace. All employees and
• applicants for employment shall be given fair treatment and equal employment
• opportunity regardless of their race, ethnicity or ancestry, color or caste,