Contractor Technical Product Manager

Key Responsibilities:
Requirement Gathering & Prioritization: Collaborate with stakeholders to define, document, and prioritize requirements for dynamic pricing and ML-driven projects.
Product Roadmap: Develop and maintain an ML product roadmap aligned with business goals, ensuring clear prioritization and communication with all stakeholders.
Sprint Planning & Delivery: Work closely with engineering and data science teams to plan and execute sprint cycles, ensuring timely and high-quality delivery of ML solutions.
Stakeholder Communication: Act as the primary liaison b/w technical teams and business stakeholders, providing updates, gathering feedback, and ensuring alignment on priorities.
Risk Mitigation: Identify and address risks related to project deliverables, timelines, and resources, ensuring smooth execution across teams.
Reporting & Analytics: Develop clear and concise reporting mechanisms to communicate project progress, KPIs, and outcomes to stakeholders and leadership.
Cross-functional collaboration: Partner with engineers, data scientists, and program managers to align efforts, troubleshoot challenges, and optimize processes.
Program Management: Oversee the end-to-end delivery of AI/ML solutions, ensuring alignment across multiple teams and facilitating seamless collaboration.

Qualifications:
5+ years of experience in product management in technology, data science, or ML domains.
Proven experience in planning and prioritizing sprint cycles, managing cross-functional teams, and mitigating risks for timely delivery.
Understanding of the DS lifecycle, data pipelines, model development, and deployment
Excellent written & verbal communication skills, with the ability to simplify complex technical concepts for diverse audiences.
Proficiency with agile methodologies and tools (e.g., Jira, Confluence, Trello) for sprint planning and tracking and familiarity with visualization tools (e.g., Tableau, Power BI) for progress tracking and KPI reporting.
Understanding of cloud platforms (e.g., AWS, Azure, Google Cloud Platform) and ML workflows (e.g., MLflow, Databricks) is a plus.