The DSAI Lead is responsible for driving business impact\nthrough the strategic delivery of Data Science and AI (DSAI) projects. This\nrole leads a multidisciplinary team and works closely with Product Management,\nIT, Climate, Risk, GIS and Business teams to ensure the successful\nimplementation of data\-driven solutions. The incumbent ensures that the DSAI\noperating model functions effectively and evolves to meet organizational needs.
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Key Responsibilities:<\/b>
<\/p>- Project\n Leadership & Delivery<\/b>
<\/li>- Leads\n the end\-to\-end lifecycle of DSAI solutions, including design, planning,\n development, testing, deployment, and value realization.
<\/li> - Oversees\n data collection, exploratory data analysis (EDA), model development,\n evaluation, and deployment.
<\/li> - Collaborates\n with data leadership to track project progress and ensure alignment with\n business objectives.
<\/li><\/ul> - Cross\-functional\n Collaboration<\/b>
<\/li>- Partners\n with Product Managers and the PMO to align project goals and execution.
<\/li> - Coordinates\n with IT teams for infrastructure provisioning, deployment, and support.
<\/li> - Works\n closely with Climate, Risk, GIS and Business teams to integrate domain\n expertise into DSAI solutions.
<\/li><\/ul> - Domain\n Expertise<\/b>
<\/li>- Applies\n experience in peril modelling for perils such as floods, inundation,\n cyclones, hailstorms, etc including supported perils.
<\/li> - Ensures\n scientific accuracy and relevance in modelling approaches.
<\/li><\/ul> - Operational\n Excellence<\/b>
<\/li>- Maintains\n and enhances the DSAI operating model to improve efficiency and\n productivity.
<\/li> - Recommends\n and implements best practices, tools, and frameworks for solution\n development.
<\/li><\/ul> - Team\n Management<\/b>
<\/li>- Manages\n the team\u2019s skill matrix and develops a structured learning calendar.
<\/li> - Oversees\n performance management processes including goal\-setting, mid\-year\n reviews, and annual evaluations.
<\/li> - Fosters\n a culture of innovation, accountability, and continuous improvement.
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Requirements<\/h3>
Qualifications / Skills / Experience:<\/b>
<\/p>- Demonstrated\n experience in leading data science and AI teams.
<\/li> - Strong\n foundation in statistical modelling, machine learning, and AI deployment.
<\/li> - Experience\n in peril modelling, climate risk, insurance and geospatial analytics.
<\/li> - Excellent\n stakeholder management and cross\-functional collaboration skills.
<\/li> - Proficiency\n with modern data science tools, Azure cloud platform, and deployment\n frameworks.
<\/li> - Minimum\n 7\-8 years in data science/AI roles, with at least 2\-3 years in a lead\n position.
<\/li> - Familiarity\n with agile project management and enterprise IT environments.
<\/li> - Strong\n leadership, communication, and mentoring abilities.
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