Head - Data Science

As a Lead Data Scientist at the company, you will be responsible for building and leading a top-tier Data Science team that drives significant business outcomes at scale. This is a role that requires both strategic thinking and hands-on execution, where you will identify key opportunities, shape the ML roadmap, conduct rapid experimentation, and deploy production-grade ML systems to impact core metrics. Key Responsibilities: - Set the standard for exceptional Data Science and ML execution company-wide. - Define and execute the Data Science roadmap by targeting high-impact problem areas. - Recruit, develop, and mentor a talented team of Data Scientists and ML Engineers with a focus on ownership, urgency, and technical excellence. - Manage end-to-end delivery of ML initiatives, from initial business framing to deployment, monitoring, and continuous enhancement. - Act as a player-coach by supporting teams, evaluating modeling approaches, challenging assumptions, and getting hands-on as needed. - Collaborate closely with Product, Engineering, and Business leaders to align on priorities, define success metrics, and ensure solution adoption. - Develop scalable, production-grade ML systems with robust engineering practices and long-term sustainability. - Cultivate a culture of high-velocity experimentation with clear hypotheses and a sharp focus on business impact. - Establish ML best practices across the lifecycle, including feature engineering, validation, deployment, monitoring, retraining, governance, and documentation. - Drive the team towards decision automation, optimization, and intelligent systems beyond traditional models. - Communicate effectively and influence decisively, particularly in complex or high-stakes cross-functional scenarios. - Elevate the organization's ML maturity by fostering trust, adoption, and understanding of Data Science across various teams. Qualifications Required: - 9+ years of experience in Data Science and Machine Learning, with a minimum of 2 years leading or managing high-performing DS/ML teams. - Demonstrated success in deploying ML systems that have had a measurable business impact on a large scale. - Proficiency in applied machine learning, statistical modeling, and optimization, particularly in dynamic business environments. - Hands-on expertise with ML frameworks and libraries like PyTorch, TensorFlow/Keras, Scikit-learn. - Strong knowledge of core ML methods such as Regression, Classification, Tree-based/Gradient Boosting models, and Time-Series Forecasting. As a Lead Data Scientist at the company, you will be responsible for building and leading a top-tier Data Science team that drives significant business outcomes at scale. This is a role that requires both strategic thinking and hands-on execution, where you will identify key opportunities, shape the ML roadmap, conduct rapid experimentation, and deploy production-grade ML systems to impact core metrics. Key Responsibilities: - Set the standard for exceptional Data Science and ML execution company-wide. - Define and execute the Data Science roadmap by targeting high-impact problem areas. - Recruit, develop, and mentor a talented team of Data Scientists and ML Engineers with a focus on ownership, urgency, and technical excellence. - Manage end-to-end delivery of ML initiatives, from initial business framing to deployment, monitoring, and continuous enhancement. - Act as a player-coach by supporting teams, evaluating modeling approaches, challenging assumptions, and getting hands-on as needed. - Collaborate closely with Product, Engineering, and Business leaders to align on priorities, define success metrics, and ensure solution adoption. - Develop scalable, production-grade ML systems with robust engineering practices and long-term sustainability. - Cultivate a culture of high-velocity experimentation with clear hypotheses and a sharp focus on business impact. - Establish ML best practices across the lifecycle, including feature engineering, validation, deployment, monitoring, retraining, governance, and documentation. - Drive the team towards decision automation, optimization, and intelligent systems beyond traditional models. - Communicate effectively and influence decisively, particularly in complex or high-stakes cross-functional scenarios. - Elevate the organization's ML maturity by fostering trust, adoption, and understanding of Data Science across various teams. Qualifications Required: - 9+ years of experience in Data Science and Machine Learning, with a minimum of 2 years leading or managing high-performing DS/ML teams. - Demonstrated success in deploying ML systems that have had a measurable business impact on a large scale. - Proficiency in applied machine learning, statistical modeling, and optimization, particularly in dynamic business environments. - Hands-on expertise with ML frameworks and libraries like PyTorch, TensorFlow/Keras, Scikit-learn. - Strong knowledge of core ML methods such as Regressi