• Experience in guiding teams– from a technical and career-wise perspective–and should be able to set and build an open and innovative culture in the team. Lead a team size of 10-15 data scientists.
• Recruit, develop, and retain a high-performing team of data scientists and analysts.
• Provide coaching, mentorship, and professional development opportunities for high impact team.
• Set performance expectations and provide regular feedback to ensure the team's success.
• Allocate resources for continuous learning, certifications, and training programs for team members.

3. Project Management:

Oversee the planning, execution, and delivery of data science projects, ensuring they are completed on time.

Collaborate with cross-functional teams to define project requirements and deliverables.

Monitor project progress and identify and address any issues or risks that may arise.

Establish clear metrics or KPIs for evaluating the success of data science projects and team performance.

4. Stakeholder Management:

Engage with stakeholders to understand their needs and requirements.

Communicate the value of AI research to internal and external stakeholders.

Build relationships with academic institutions and other research organizations.

What makes you a great fit:

• Evaluate and recommend new tools, technologies, and methodologies to enhance the data science capabilities of the team.
• Expertise in machine learning frameworks (e.g., TensorFlow, PyTorch) and data science tools (e.g., Python, R)
• Ensure that the team has access to the necessary data, tools, and infrastructure to perform their work effectively.
• Implement data governance frameworks and ensure data quality standards are met.
• Manage the scalability and deployment of data science solutions in production environments.

1. Communication and Collaboration:

• Communicate complex technical concepts and findings to non-technical stakeholders in a clear and concise manner.
• Collaborate with business leaders to identify opportunities for leveraging data to drive business value.
• Build strong relationships with internal and external partners to support the data science team's objectives.

2. Compliance and Ethics:

• Ensure that data science activities comply with relevant laws, regulations, and ethical guidelines.
• Promote a culture of data privacy and security within the team and across the organization. Create a roadmap for the data science team in consultation with the company leadership team.

3. Leadership:

• Complex Problem Solving: Address and resolve complex challenges related to data quality, model interpretability, and stakeholder expectations.
• Collaboration with business units across the organization to identify and refine opportunities for data-driven features.
• Creating strategy and roadmap for Data Science Team using latest trends and technologies, create newer technical solutions and convert in offerings
• Ability to create and manage external partnership with industry, academia
• Ability to drive customer conversations with right value proposition
• Excellent communication and presentations skill with ability to lead, mentor and coach the team
• Experience of People Management & Project Management experience