Senior/Staff Data Scientist

Who We Are

Verve has created a more efficient and privacy-focused way to buy and monetize advertising. Verve is an ecosystem of demand and supply technologies fusing data, media, and technology together to deliver results and growth to both advertisers and publishers–no matter the screen or location, no matter who, what, or where a customer is. With offices across the globe and with an eye on servicing forward-thinking advertising customers, Verve’s solutions are trusted by more than 90 of the United States’ top 100 advertisers, 4,000 publishers globally, and the world’s top demand-side platforms. Learn more at verve.com .

Senior / Staff Data Scientist (IC4/IC5) - Yield Optimization

Responsibilities

Lead the research and modeling behind the Yield Optimization portfolio: Demand Shaping, Floor Optimization and Bid Optimization.

Turn business problems in programmatic auctions into well-posed modeling problems, with clear objectives, constraints and success metrics such as revenue and margin per bid request.

Build and validate models for bidding, reserve prices and traffic shaping, using auction theory, causal inference, forecasting and online learning methods such as bandits.

Know the bid-stream data in depth: find biases, leakage and data-quality problems, and design features that hold up in production.

Identify and champion new methods to maintain Verve's competitive edge in yield optimization.

Plan and execute large-scale initiatives with significant impact, managing multiple interdependencies and cross-team collaborations.

Work with ML engineers to take models to production: set the latency and feature requirements, review the serving design, and own model quality after launch.

Act as a technical leader and mentor to other data scientists and engineers across distributed teams.

Refine team problem-solving tools, methodologies, and technical documentation to uplift the overall team competency.

Partner closely with Product Management, ML Engineering and Backend Engineering to align long-term technical roadmaps with business strategy.

Stay hands-on: write production-quality model code, run the statistical analysis behind major decisions, and design and read A/B tests and offline evaluations.

Required Skills

5+ years in data science or applied ML, with models you built running in production and a measurable business impact.

Extensive AdTech industry expertise, with deep knowledge of programmatic auctions, supply/demand matching, and real-time bidding optimization.

Strong foundations in statistics, probability and optimization, with applied experience in at least one of auction theory, causal inference or experimental design.

Master-level proficiency in Python and SQL .

Deep expertise in ML methods and frameworks ( Scikit-learn , gradient boosting, PyTorch or TensorFlow ), including model selection, validation and offline evaluation.

Hands-on analysis of large datasets with BigQuery and PySpark , and model training on a cloud platform such as Vertex AI .

Working knowledge of real-time model serving, enough to design models that meet latency and feature constraints.

Exceptional communication skills with the ability to influence technical and non-technical stakeholders across the organization.

Ability to navigate and align multiple teams around long-term technical visions and modeling standards.

Advanced degree (Master's or PhD preferred) in a quantitative field (Applied Mathematics, Statistics, Computer Science, or Physics).

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