Product Manager (Data Science)

Product Manager – Data Science
Location-Orlando, FL (3-4 Days Hybrid)
12 months

Experience: 12 + Years 

This role requires strong expertise in SQL, Python, R Programming, Machine Learning, Artificial Intelligence, Data Analytics, Product Management, and Agile methodologies.

Key Responsibilities

Product Strategy

• Define product vision, roadmap, and long-term strategy.
• Identify business opportunities using data analytics and AI.
• Prioritize features based on customer value, ROI, and business impact.
• Create product requirement documents (PRDs), user stories, and acceptance criteria.
• Conduct market research and competitive analysis.

Data Science & Analytics

• Analyze large structured and unstructured datasets.
• Design analytical frameworks to support strategic decisions.
• Perform statistical analysis using SQL, Python, and R.
• Define KPIs and product success metrics.
• Interpret customer behavior through data analysis.
• Drive experimentation through A/B testing and hypothesis validation.

AI / Machine Learning

• Work with Data Scientists to design AI/ML solutions.
• Identify machine learning use cases aligned with business objectives.
• Understand supervised, unsupervised, reinforcement learning, and deep learning concepts.
• Evaluate model performance and business impact.
• Collaborate on Generative AI and Large Language Model (LLM) use cases.
• Support deployment, monitoring, and continuous improvement of ML models.

Product Ownership

• Own the product backlog.
• Prioritize sprint deliverables.
• Work closely with Engineering, UX, QA, and Data teams.
• Lead sprint planning, backlog grooming, and sprint reviews.
• Ensure timely product delivery.

Stakeholder Management

• Collaborate with business leaders and technical teams.
• Translate technical findings into business recommendations.
• Present dashboards, reports, and product insights to executive leadership.
• Manage cross-functional communication throughout the product lifecycle.

Product Analytics

• Monitor adoption, engagement, retention, and customer satisfaction metrics.
• Perform root cause analysis for product issues.
• Recommend product enhancements based on customer data.
• Measure business outcomes after product releases.