We are seeking a highly skilled Data Scientist 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 :

- Bachelors degree in Data Science, Computer Science, Business, or a related field (Masters 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).