Project Manager – Data Science

Project Manager – Data Science

Location: Remote (Open to candidates from any country)

Role Overview

The Data Science Project Manager (PM) is responsible for planning, coordinating, and delivering data-driven projects. They act as the bridge between data scientists, engineers, business stakeholders, and leadership to ensure projects are completed on time, within scope, and aligned with business goals. Unlike a purely technical role, the PM focuses on execution, communication, and strategy while having enough understanding of data science concepts to manage effectively.

Key Responsibilities

• Project Planning & Execution
  • Define project scope, goals, deliverables, timelines, and resources.
  • Develop and manage project roadmaps, sprint planning, and task tracking (Agile/Scrum, Kanban, or hybrid).
  • Ensure alignment of data science projects with organizational strategy.
• Stakeholder Management
  • Act as the point of contact between technical teams and business leaders.
  • Translate business needs into technical requirements and vice versa.
  • Manage expectations, communicate risks, and provide regular project updates.
• Team Coordination
  • Coordinate activities across data scientists, data engineers, analysts, and software developers.
  • Facilitate collaboration between cross-functional teams.
  • Remove blockers and resolve conflicts within the team.
• Quality & Risk Management
  • Monitor project risks, dependencies, and constraints.
  • Ensure proper data governance, compliance, and ethical AI considerations.
  • Oversee testing, validation, and delivery of ML models or analytics solutions.
• Performance & Delivery
  • Track KPIs, project milestones, and success metrics.
  • Ensure projects deliver actionable insights or deployable AI/ML solutions.
  • Manage project budgets and resources effectively.

Required Skills & Competencies

• Project Management Skills: Agile, Scrum, Kanban, PMP or PRINCE2 certification (a plus).
• Data Science Awareness: Understanding of machine learning, analytics, and data engineering workflows (not necessarily coding expertise).
• Communication & Leadership: Strong stakeholder communication, conflict resolution, and team leadership skills.
• Analytical Thinking: Ability to translate data-driven results into business impact.
• Tools: Jira, Trello, Asana, MS Project, Confluence, or similar project management tools.