Technical Product Manager - AI & Data Analytics
We aren’t just looking for someone to manage a backlog; we’re looking for the founding architect of our customer-facing data and AI strategy.
As a Technical Product Manager (Data & AI), you will sit at the high-impact intersection of cutting-edge Data Science, modern Engineering, and customer-facing value. This is a highly visible, strategic role where you’ll partner directly with executive-level clients to co-develop the predictive models, analytical features, and SaaS data products that will define our industry.
If you want to move away from internal tooling and actually ship AI/ML features that drive massive, quantifiable ARR, this is your playground.
WHAT YOU'LL DO:Shape the AI & Data Vision
Own the Predictive Roadmap: Drive the vision and strategy for our external AI/ML models, advanced analytics features, and data products.
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.
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
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.
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).
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
3+ years of relevant experience across Product Management, Data Analytics, or Data Engineering.
Strong Business & Product Acumen: The ability to look at a complex technical feature and immediately see how it connects to customer ROI.
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).
Data Visualization Chops: Experience with a modern BI platform (e.g., Sigma, Tableau, Looker) and a strong sense of how to present data clearly.
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.
Great product minds don’t all follow the same blueprint. You likely fall into one of these buckets:
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.
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.
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.
3+ years of relevant experience across Product Management, Data Analytics, or Data Engineering.
Strong Business & Product Acumen: The ability to look at a complex technical feature and immediately see how it connects to customer ROI.
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).
Data Visualization Chops: Experience with a modern BI platform (e.g., Sigma, Tableau, Looker) and a strong sense of how to present data clearly.
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.
Our Stack: Experience with (or a burning desire to learn) our modern data stack: Snowflake, DBT, Fivetran, and Sigma.
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.
Coding Lite: Familiarity with Python or R for lightweight data analysis.
AI/ML Exposure: Experience working around the machine learning lifecycle or productionized models.