Senior Product Manager

Smadex SLU•New York, NY

6d•Hybrid

About The Position

As a Senior Product Manager on our measurement and data team, you will own a set of quantitatively deep initiatives that sit close to our bidding, attribution, and data infrastructure. This is a builder role for someone who is comfortable reasoning about causal inference, experimentation, and data quality at scale, and who can turn that reasoning into a product that engineers, data scientists, and commercial teams actually use. You will work directly with our ML, data, and infrastructure teams as a technical peer. This position is based in Austin, TX or New York, NY.

Requirements

Technical degree in a quantitative field such as Engineering, Statistics, Mathematics, Economics, or Data Science.
5+ years in product management or data-heavy product work, including time owning measurement, ML-adjacent, or data-platform products.
Strong applied grounding in at least one of: causal inference and incrementality measurement, experimentation and A/B testing, attribution and identity, or large-scale data quality and observability.
Fluency with data. You can write SQL, reason about a data model, and work independently in a notebook or query engine to answer your own questions.
Comfort engaging directly with model behavior, evaluation, and statistical tradeoffs alongside data scientists and ML engineers.
Demonstrated experience in hypothesis-driven learning and turning data into actionable insights.
Excellent written communication. You write specs that hold up to technical scrutiny and need little editing before they ship.

Nice To Haves

Experience in programmatic advertising, adtech, or another real-time bidding or marketplace environment.
Familiarity with mobile attribution, MMPs, or privacy-driven measurement.

Responsibilities

Own product strategy and execution for measurement and data initiatives spanning causal measurement, attribution and identity, and the quality and observability of our data and models.
Translate causal and statistical methods into product decisions, and partner with data science on how measurement and optimization should behave in production.
Define how performance signals are monitored, explained, and surfaced across large, fast-moving datasets so the right people see what changed and why.
Work closely with engineering to prioritize development, and align commercial, marketing, and operations teams on timelines and strategy.
Set the metrics and experimentation approach for your area, and use them to decide what ships.
Stay at the forefront of AdTech, new technologies, industry trends, and privacy regulations.