Senior Product Manager

As a Senior Product Manager at INCRMNTAL, you will sit at the intersection of data science, engineering, and product, translating complex ML and causal inference capabilities into a product our customers love. You will work hand-in-hand with our data science and the engineering teams to shape how models are designed, evaluated, and delivered into production. Own a significant part of the product roadmap for our value measurement platform, with a focus on data science driven features (causal inference, incrementality modeling, attribution, and media mix optimization) and customer onboarding.

Partner closely with data scientists from problem framing through experimentation, model validation, and productization, you’ll be expected to critically read model specs, challenge assumptions, and help prioritize research directions based on customer impact

Define success metrics and evaluation frameworks for ML models alongside the DS team, balancing statistical rigor with business usability

Own the UX of data-heavy features: collaborate with designers and data scientists to turn complex model outputs into intuitive user flows, clear visualizations, and dashboards that customers can actually act on

Design and prioritize dashboards, reports, and in-product analytics, defining what metrics to surface, how to visualize uncertainty and causal results, and how to make insights discoverable without overwhelming the user

Run user research and customer interviews to validate both the analytical value and the usability of features, and onboarding integration; translate findings into concrete design and product improvements

Translate customer problems into well-scoped technical and design requirements; translate model outputs and methodology into clear value propositions for customers

Prepare the implementation of features and support engineering, design, and DS teams through design reviews, QA, and launch, including writing acceptance criteria that account for model behavior, edge cases, data quality, and UX states (empty, loading, partial data, error)

Ensure data integrity through testing, acceptance tests, and ongoing monitoring across our diverse range of data products

Work with clients and internal stakeholders to understand business goals, identify relevant KPIs, and feed those insights back into the product, modeling, and dashboarding roadmap

Oversee end-to-end data flow and engineering processes, collaborating with ML, data, and engineering teams to ensure efficient and accurate operation during critical phases like client onboarding and daily production runs

Strategically plan for missing or imperfect data, designing product behavior and UX for new client onboarding, process failures, client-specific configurations, long-term seasonality, and external signals/features

Drive experimentation: help design A/B tests, back-tests, and validation studies to assess the impact of new models, features, and UX changes

Key Requirements

Min. 4 years of work experience in a product management role, experience in SaaS, B2B and/or data-heavy / ML-driven products is strongly preferred

Proven track record of shipping products that involve machine learning, statistical modeling, or data pipelines, comfortable being the PM counterpart to a data science team

Technical fluency: able to read and discuss ML/statistical concepts (regression, causal inference, Bayesian methods, time series) with data scientists, and able to reason about trade-offs in model design

Hands-on comfort with SQL and the ability to independently query, explore, and validate data; familiarity with Python (notebooks, basic scripting) is a strong plus

Understanding of data infrastructure concepts, ETL/ELT pipelines, data quality, distributed processing (Spark, Kubernetes, Argo, or similar), enough to collaborate meaningfully with engineering

Strong UX sensibility for data products, experience partnering with designers to turn complex data and model outputs into clear, usable interfaces; comfort working in Figma (reviewing, commenting, and prototyping); a strong eye for data visualization and how to communicate uncertainty, causal results, and KPIs to non-technical users

Experience shaping dashboards and in-product analytics, defining what to surface, how to structure it, and how users will interpret and act on it

Hands-on experience with product integrations and data connectors, understanding how customers bring their data in (APIs, SDKs, MMPs, ad platforms, server-to-server integrations, data warehouses), and how to design onboarding flows that handle authentication, data mapping, validation, and error states

Familiarity with third-party integrations in the ad-tech ecosystem (e.g., Google Ads, Meta, TikTok, AppsFlyer, Adjust, Snowflake, BigQuery) is a big plus

Knowledge of agile development practices (Scrum, Kanban)

Strong analytical skills, conceptual thinking, and a structured work approach

Excellent written and verbal communication, able to translate between DS, engineering, design, and commercial stakeholders

Familiarity with advertising, digital marketing, analytics, attribution, or tracking is a big plus BSc or higher in a quantitative field (Computer Science, Statistics, Mathematics, Engineering, Physics, or similar) is a plus