Technical Product Manager - AI & Data Analytics
At a glance
Manager-level Product Management role at WorkWave. Remote, United States · full-time · $155,000–$160,000 base.
remoteroles summary, based on the employer's posting.
What you'll do
- Set direction for customer-facing AI models, analytics features and data products
- Work with key customers to test product ideas, identify user needs and shape features together
- Turn customer problems into engineering requirements, data rules and technical specifications
- Partner with UX and data science to make complex analysis clear in visualizations and demos
- Check product assumptions against data, then coordinate launches with marketing and sales around adoption, retention and ARR
What you bring
- At least three years in product management, data analytics or data engineering
- You can use SQL joins and aggregations to investigate data independently
- You’ve worked with a BI platform such as Sigma, Tableau or Looker and can present data clearly
- You can connect technical work to customer returns and communicate with executive clients, sales teams and engineers
Who this fits
This suits a data product manager, SaaS product manager with strong analytics skills, or analyst ready to take on product ownership. The role is remote in the US, and applicants must already have US work authorization; visa sponsorship is unavailable.
From the employer
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.
This job is remote in the US
Please note: Applicants must be currently authorized to work in the United States on a full-time basis. The company does not currently or in the future provide sponsorship for employment visa status.
WHAT YOU'LL DO:
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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.
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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.
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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.
Shape the AI & Data Vision
Execute & Ship with Impact
What You’ll Bring to the Table
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).
-
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.