The DSAI Lead is responsible for driving business impact
through the strategic delivery of Data Science and AI (DSAI) projects. This
role leads a multidisciplinary team and works closely with Product Management,
IT, Climate, Risk, GIS and Business teams to ensure the successful
implementation of data-driven solutions. The incumbent ensures that the DSAI
operating model functions effectively and evolves to meet organizational needs.

Key Responsibilities:
- Project
Leadership & Delivery
- Leads
the end-to-end lifecycle of DSAI solutions, including design, planning,
development, testing, deployment, and value realization.
- Oversees
data collection, exploratory data analysis (EDA), model development,
evaluation, and deployment.
- Collaborates
with data leadership to track project progress and ensure alignment with
business objectives.
- Cross-functional
Collaboration
- Partners
with Product Managers and the PMO to align project goals and execution.
- Coordinates
with IT teams for infrastructure provisioning, deployment, and support.
- Works
closely with Climate, Risk,



GIS and Business teams to integrate domain
expertise into DSAI solutions.
- Domain
Expertise
- Applies
experience in peril modelling for perils such as floods, inundation,
cyclones, hailstorms, etc including supported perils.
- Ensures
scientific accuracy and relevance in modelling approaches.
- Operational
Excellence
- Maintains
and enhances the DSAI operating model to improve efficiency and
productivity.
- Recommends
and implements best practices, tools, and frameworks for solution
development.
- Team
Management
- Manages
the team’s skill matrix and develops a structured learning calendar.
- Oversees
performance management processes including goal-setting, mid-year
reviews, and annual evaluations.
- Fosters
a culture of innovation, accountability, and continuous improvement.

Requirements
Qualifications / Skills / Experience:
- Demonstrated
experience in leading data science and AI teams.
- Solid
foun

📌 Data Science (Hyderabad)
🏢 Kshema
📍 Hyderabad