Technical Product Manager - AI & Data Analytics

We are seeking a results-oriented Product Manager to drive the vision and execution of our customer-facing SaaS products, data-driven features, and AI/ML solutions. This role is a critical bridge, translating deep technical capabilities from data science and engineering into clear, quantifiable business value for our B2B SaaS customers.

This is a highly visible, customer-facing role that involves working directly with key clients to co-develop innovative solutions. The ideal candidate is a curious, data-minded problem-solver who is excited to bridge the gap between business needs and technical solutions. This is a growth role where you will have a major impact on our product and our customers.

WHAT YOU'LL DO

Shape the AI & Data Vision

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• Own the Predictive Roadmap: Drive the vision and strategy for our external AI/ML models, advanced analytics features, and data products.
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• Co-Innovate with Clients: Embed deeply with strategic customers and design partners to validate ideas, uncover raw user needs, and co-develop next-gen features.
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• Tell the Story of Data: Partner with UX and Data Science to turn complex backend algorithms into beautiful, intuitive data visualizations and high-impact product demos.

Execute & Ship with Impact

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Translate Complexity: Act as the ultimate bridge. Translate messy customer business problems into precise technical specs, transformation logic, and data requirements for our engineers and data scientists.

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Own the GTM: Collaborate with Product Marketing and Sales to launch features that aren't just technically impressive, but actually move the needle on SaaS metrics (Adoption, Retention, ARR expansion).

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Hypothesize & Validate: Use your analytical skills to dive into data assets, validate assumptions, and prove (or disprove) product hypotheses before writing code.

What You’ll Bring to the Table

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3+ years of relevant experience across Product Management, Data Analytics, or Data Engineering.

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Strong Business & Product Acumen: The ability to look at a complex technical feature and immediately see how it connects to customer ROI.

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SQL Fluency: You are comfortable writing joins and aggregations to explore data yourself. (We care about your analytical problem-solving, not whether you memorized advanced syntax).

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Data Visualization Chops: Experience with a modern BI platform (e.g., Sigma, Tableau, Looker) and a strong sense of how to present data clearly.

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The "Translator" Superpower: Elite communication skills. You can pitch a vision to an executive client, align a sales team, and debate data modeling with an engineer.

WHO YOU ARE

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Great product minds don’t all follow the same blueprint. You likely fall into one of these buckets

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The Seasoned Data PM: You already build data products but want a more visible, customer-facing role where you can co-develop directly with clients on a modern stack.

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The Data-Obsessed SaaS PM: You manage a standard SaaS product, but you secretly spend all your time in SQL, building dashboards, and lurking in the analytics team's Slack channels. You're ready to make Data/AI your full-time focus.

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The Product-Minded Analyst: You're a Senior Data Analyst or BI Developer who doesn't just answer "what happened?" but actively tells stakeholders "what's next." You’ve been "unofficially" product-managing your work for a while and are ready to make it official.

WHAT YOU'LL BRING

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3+ years of relevant experience across Product Management, Data Analytics, or Data Engineering.

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Strong Business & Product Acumen: The ability to look at a complex technical feature and immediately see how it connects to customer ROI.

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SQL Fluency: You are comfortable writing joins and aggregations to explore data yourself. (We care about your analytical problem-solving, not whether you memorized advanced syntax).

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Data Visualization Chops: Experience with a modern BI platform (e.g., Sigma, Tableau, Looker) and a strong sense of how to present data clearly.

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The "Translator" Superpower: Elite communication skills. You can pitch a vision to an executive client, align a sales team, and debate data modeling with an engineer.

WHAT WOULD ALSO BE AWESOME

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Our Stack: Experience with (or a burning desire to learn) our modern data stack: Snowflake, DBT, Fivetran, and Sigma.

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Industry Context: Experience in complex B2B SaaS verticals—especially physical operations like Field Services (logistics, routing, dispatch) or Building Services—is a massive plus. These industries are sitting on massive, unoptimized datasets ripe for AI disruption.

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Coding Lite: Familiarity with Python or R for lightweight data analysis.

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AI/ML Exposure: Experience working around the machine learning lifecycle or productionized models.