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.