Data Science Project Manager

About the job

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).