| Technology Project Manager - Data Science Evaluation Project, Remote at Remote, Remote, USA |
| Email: [email protected] |
| From: Shubhra Soni, sonitalent [email protected] Reply to: [email protected] Hii, Hope you are doing well , We are looking for Technology Project Manager - Data Science Evaluation Project if you are interested kindly send me your resume. Job Title - Technology Project Manager - Data Science Evaluation Project Job Location - Remote Duration -6 Months+ Mode Of Interview Phone/Skype Note -Need LinkedIn Job Description This is a multifaceted role that encompasses project management, data science understanding, and technical expertise. Key Responsibilities: 1. Project Planning and Management: Develop and manage a detailed project schedule and work plan. Run Standups and manage a Kanban board for a team of several engineers. Provide project updates on a consistent basis to various stakeholders about strategy, adjustments, and progress. 2. Team Leadership and Coordination: Lead, coordinate, and support internal teams and external partners. Ensure resources and team members are aligned to meet project deadlines and objectives. 3. Data Science Understanding: Possess a strong understanding of data science concepts to effectively communicate with data scientists and understand project needs. Facilitate the integration of data science methodologies and tools into the project. 4. Technical Expertise: Oversee and guide the technological aspects of the project. Ensure the project aligns with the latest technological trends and data science best practices. 5. Budgeting and Resource Allocation: Manage project finances, including budgeting and resource allocation. Monitor and report on financial performance of the project. 6. Stakeholder Engagement: Maintain relationships with stakeholders, including clients, management, and team members. Regularly update them on project status and take feedback to incorporate into project planning. 7. Risk Management: Identify and mitigate potential risks throughout the project lifecycle. Implement risk management strategies to minimize project delays and cost overruns. 8. Quality Assurance: Ensure the projects deliverables meet quality standards and client expectations. Implement continuous improvement practices throughout the project lifecycle. 9. Documentation and Reporting: Maintain comprehensive project documentation, reports, and plans. Ensure all project documents are complete, current, and stored appropriately. 10. Compliance and Standards: Adhere to and enforce all applicable laws, regulations, policies, and standards relevant to data science and technology projects. 11. Ability to speak to and engage in a highly effective way with users/customers who are data scientists, health researchers Keywords: |
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| Fri Feb 02 02:58:00 UTC 2024 |