Note: The job is a remote job and is open to candidates in USA. Scalence L.L.C. is seeking a Technical Product Owner to own the delivery of AI/ML models for a defined product cluster. The role manages the AI Layer backlog, translates product priorities into technical workstreams, oversees DS-to-MLE productionization handoffs, and facilitates agile delivery while improving efficiency through AI and workflow automation.
Responsibilities
- Own and maintain a prioritized AI Layer backlog for the assigned product cluster, with DS and MLE work represented as distinct, sequenced backlog items
- Translate product-level priorities from the Product Manager into AI Layer workstreams with clear, technically specific acceptance criteria for both DS and MLE work
- Write acceptance criteria for DS work (model performance thresholds, evaluation methodology, holdout set specification, model card completeness) and MLE work (serving latency SLOs, monitoring requirements, rollback procedures) separately
- Own the DS-to-MLE Handoff Review ceremony: ensure model readiness criteria — including eval documentation, serving requirements, and monitoring criteria — are fully met before MLE operationalization work enters a sprint
- Partner with the Tech Lead at every backlog refinement to validate feasibility, surface technical risks, and confirm story scope and sizing before sprint commitment
- Make sprint-level trade-off decisions — scope, quality threshold, experiment continuation or termination — with authority and appropriate speed
- Facilitate sprint planning, backlog refinement, sprint demo, and retrospective ceremonies for the assigned team
- Surface external dependency blockers to the appropriate owner immediately
- Shield the team from unplanned work and context-switching by enforcing backlog discipline and managing stakeholder expectations
- Continuously improve team leverage through AI, agents, and workflow automation
- Automate routine delivery-management activities including backlog refinement, reporting, dependency tracking, and handoff validation where appropriate
- Measure and report efficiency gains from AI-enabled delivery practices
Skills
- Proven experience as a Product Owner or Product Manager for AI/ML or data science product delivery
- Direct experience working with cross-functional teams of data scientists and ML engineers in a production ML environment
- Ability to read and interpret model evaluation results, experiment logs, and ML pipeline outputs as an informed decision-maker — not necessarily as a practitioner
- Demonstrated ability to write technically specific acceptance criteria for both DS model work and MLE productionization work
- Experience distinguishing and sequencing research/experimentation work from engineering/production work in a backlog
- Experience with the DS-to-MLE model productionization handoff: what constitutes a complete handoff, what risks arise from incomplete ones
- Strong prioritization and trade-off decision-making skills in an environment of high technical uncertainty
- Ability to work effectively across multiple tasks and teams while meeting aggressive timelines
- Outstanding written and verbal communication skills; able to explain ML model trade-offs and delivery risks to non-technical stakeholders
- Bachelor's degree or above in Computer Science, Data Science, Statistics, or related field; Master's degree preferred
- 2–3 years of experience in roles as Product Owner, Product Manager, or technical delivery lead for AI/ML or data science products
- Demonstrated track record of shipping ML models into production across the full lifecycle: from problem framing through monitoring
- Background in data science, machine learning, or a quantitative field
- Familiarity with ML experiment tracking and model lifecycle tooling
- Experience with real-time inference products and latency-sensitive ML serving requirements
- Prior experience owning or managing an ML platform or model serving infrastructure
- Experience with Databricks or equivalent cloud ML platform
- Master's degree
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