Product Manager -ML

• Contract Length: 12 months + extensions or conversion
• Location: Oakland, CA

We are seeking a highly technical Product Manager to support data‑intensive, machine learning, and algorithm‑driven products focused on risk modeling. This role is ideal for someone who thrives at the intersection of data science, engineering, and stakeholder engagement
, and is comfortable translating complex technical concepts into clear product direction.

This is a 12‑month contract role based in Oakland, CA
, operating on a hybrid schedule with on‑site collaboration required.

• Partner closely with risk model developers and data scientists to support the development, enhancement, and delivery of machine learning and algorithm‑based products.
• Own product planning activities including backlog prioritization, roadmap development, and Agile ceremonies
  .
• Act as a bridge between technical teams and business stakeholders — managing requests, aligning priorities, and educating stakeholders on model functionality, limitations, and outputs
  .
• Translate complex analytical and engineering concepts into clear, actionable product requirements and narratives.
• Support cross‑functional communication to ensure alignment across engineering, data science, and leadership teams.

• Strong understanding of electric utility systems
  , including exposure to grid operations, asset performance, and system failure modes across transmission and distribution.
• Experience working on data science and machine learning–driven products
  , with enough technical depth to collaborate effectively with data scientists and engineers.
• Hands‑on experience operating in Agile environments
  , including serving as a SCRUM Master and/or leading Agile product management practices
  .
• Proven ability to manage complex stakeholder relationships in highly technical domains.
• Background in data science, engineering, physics, or another scientific discipline is a strong plus.
• Advanced degree (MS or PhD preferred) in Engineering, Data Science, Computer Science, Physics, or a related field is ideal.

• Utility industry experience, particularly related to risk modeling, infrastructure resilience, or wildfire risk
  .
• Prior experience supporting model‑driven or algorithmic decision systems
  .
• Comfort working in ambiguous, evolving problem spaces with high technical complexity.