Technical Project Manager

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).