Technical Product Owner

• Product Vision & Roadmap: Define and maintain a technical roadmap for AI/ML products, ensuring alignment with organizational goals and infrastructure capabilities.
• Backlog Management: Author detailed technical user stories and acceptance criteria. Translate high-level business requirements into technical tasks for data scientists and ML engineers (e.g., specifying performance metrics like F1-score, precision/recall, or latency requirements).
• Technical Stewardship: Collaborate with architects to ensure data quality, governance, and scalable model deployment strategies.
• Stakeholder Management: Act as the primary interface between business units and the technical team, managing expectations regarding the iterative and probabilistic nature of AI development.
• MLOps & Delivery: Oversee the integration of models into production environments, ensuring robust monitoring, feedback loops, and continuous retraining pipelines.
• Data Strategy: Identify and prioritize data source acquisition, labeling efforts, and feature engineering requirements necessary for model success.

• Experience: 5 years of Product Management or Product Owner experience, with at least 2-3 years specifically focused on AI, Machine Learning, or Big Data products.
• Technical Depth: Strong understanding of ML fundamentals (Supervised/Unsupervised learning, NLP, Computer Vision, or LLMs) and the CRISP-DM or similar data science lifecycles.
• Tooling: Familiarity with ML frameworks (PyTorch, TensorFlow, Scikit-learn) and cloud platforms (AWS SageMaker, Azure ML, or Google Cloud Platform Vertex AI).
• Data Savvy: Proficiency in SQL and experience working with large-scale data environments (Snowflake, Databricks, or Hadoop).
• Agile Proficiency: Expert knowledge of Agile/Scrum methodologies and experience using Jira or Azure DevOps for sprint planning.
• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related quantitative field.

Salary : $50