Data Science Manager III

As a pioneer in digital outdoor navigation with a suite of apps, onX was founded in Montana, which in turn has inspired our mission to awaken the adventurer inside everyone. With more than 400 employees located around the country working in largely remote / hybrid roles, we have created regional “Basecamps” to help remote employees find connection and inspiration with other onXers.

We bring our outdoor passion to work every day, coupling it with industry-leading technology to craft dynamic outdoor experiences.

Through multiple years of growth, we haven’t lost our entrepreneurial ethos  offer a fast-paced, growing, tech-forward environment where ownership, accountability, and passion for winning as a team are essential. We value diversity and believe it leads to different perspectives and inspires both new adventures and new growth. As a team, we’re hungry to improve, value innovation, and believe great ideas come from any direction.

Important Alert :
Please note, on Xmaps will never ask for credit card or SSN details during the initial application process. For your digital safety, apply only through our legitimate website at  or directly via our Linked In page.

We're looking for an accomplished Data Science Manager to lead our data science team in delivering high-impact analytics and machine learning solutions across the business. You'll oversee a high performing team driving predictive modeling, customer insights, operational optimization, and product innovation using our rich datasets on Google Cloud Platform (GCP). Reporting directly to the VP of Data, you'll partner closely with product, engineering, and business leaders to translate data into actionable strategies.

This role emphasizes strong people leadership, agile delivery, and the ability to communicate complex insights simply to non-technical executives. Experience with geospatial data is a plus but not required—we value broad data science expertise on modern cloud infrastructure.

• Lead and mentor a team of 3 data scientists and ML engineers, focusing on technical guidance, career growth, and performance management.
• Define and prioritize the data science roadmap, aligning projects with company goals across customer behavior modeling, forecasting, recommendation systems, and operational efficiency.
• Oversee the design, development, and production deployment of advanced machine learning models and analytical solutions at scale.
• Partner with stakeholders across product, engineering, and business teams to identify high-value opportunities and deliver measurable impact through data.
• Promote best practices in data governance, model validation, and MLOps on GCP to ensure reliable, scalable solutions.
• Drive agile ways of working: backlog refinement, sprint planning, and rapid iteration to accelerate business value.
• Present insights and recommendations to executive leadership through clear storytelling, visualizations, and executive-friendly summaries.
• Keep the team up to date on GCP innovations (e.g., Vertex AI, Big Query ML) and emerging data science trends.

• 8+ years in data science, machine learning, or advanced analytics, with at least 3 years managing technical teams.
• Proven track record of delivering business impact through data science in a product-driven tech environment.
• Hands-on expertise with GCP data and ML services:
  Big Query
  , Vertex AI
  , Dataflow
  , Big Query ML
  , and related tools.
• Strong proficiency in Python, Spark, SQL, and common data science libraries (pandas, scikit-learn, Tensor Flow/PyTorch, etc.).
• Demonstrated ability to explain technical concepts and results clearly to non-technical audiences.
• Experience operating in agile environments with cross-functional collaboration.

Though not required, we would be thrilled to consider candidates with any of the following:

• Advanced degree (MS/PhD) in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.
• Familiarity with Master Data Management principles to maintain high-quality, governed reference data.
• Exposure to geospatial…