Technical Project Manager Data Science

We are seeking a highly skilled Data Science Project Manager to lead and coordinate data-driven projects from inception to completion. This role involves collaborating with cross-functional teams, ensuring timely delivery of project milestones, and effectively communicating project status and insights to stakeholders.

Key Responsibilities :

Project Leadership :

Define project scope, goals, and deliverables in collaboration with stakeholders.

Develop detailed project plans, timelines, and resource allocation strategies.

Ensure projects are completed on time, within scope, and within budget.

Stakeholder Management :

Act as the primary point of contact for project stakeholders.

Regularly communicate project progress, risks, and issues to stakeholders.

Gather and incorporate feedback from stakeholders to refine project objectives.

Data Strategy :

Collaborate with data teams to identify data needs and ensure quality data collection and management.

Analyze and interpret data to drive business decisions and strategies.

Risk Management :

Identify potential project risks and develop mitigation strategies.

Monitor project progress and implement corrective actions as necessary.

Reporting and Documentation :

Create comprehensive project documentation, including reports, dashboards, and presentations.

Track project performance and report on key metrics to senior management.

Qualifications :

Bachelor’s degree in Data Science, Computer Science, Business, or a related field (Master’s preferred).

Proven experience in project management, particularly in data science or analytics.

Strong understanding of data science methodologies, tools, and technologies (e.g., Python, R, SQL).

Experience with project management tools (e.g., Jira, Trello, Asana).

Excellent communication and interpersonal skills.

Strong analytical and problem-solving abilities.

Ability to manage multiple projects simultaneously and meet deadlines.

Preferred Skills :

Experience in Agile methodologies.

Familiarity with machine learning and statistical modeling.

Knowledge of cloud computing platforms (e.g., AWS, Azure, Google Cloud).