Data Science Offshore Lead (Life Sciences)

Role Overview: You will be responsible for leading and managing offshore data science projects in the life sciences sector. Your main tasks will involve coordinating with cross-functional teams, developing robust data science models, facilitating communication between onshore and offshore teams, staying updated on the latest advancements in data science, ensuring quality improvement and risk management, and conducting progress reviews with stakeholders. Key Responsibilities: - Lead offshore data science operations in Pune, Maharashtra, India, ensuring high-quality analytical solutions for the life sciences industry. - Partner with global stakeholders to define project scopes, objectives, and success metrics. - Foster a collaborative work environment, championing best practices and continuous improvement in data science methodologies. - Mentor and develop talent within the team, providing guidance and career development opportunities. - Identify opportunities for growth and expansion of data science capabilities, advocating for investment in new tools and technologies. - Ensure regulatory compliance and ethical standards are met in all analytical processes and project deliveries. - Drive the adoption of automated, efficient workflows to enhance analytical capabilities and performance across all offshore operations. Qualification Required: - Proven experience in leading data science teams, preferably in the life sciences sector. - Strong knowledge and practical experience in applying data science and machine learning techniques. - Advanced degree in a relevant field such as Data Science, Computer Science, or Life Sciences. - Demonstrated project management skills with the ability to oversee multiple projects simultaneously. - Excellent communication and interpersonal skills to liaise effectively with diverse teams and stakeholders. - Strong problem-solving and decision-making skills, able to translate complex data insights into actionable strategies. - Proficiency in programming languages commonly used in data science, such as Python or R, and experience with data visualization tools.,