Principal Data Science - P&C Insurance - AD- Bangalore

Principal Data Science - P&C Insurance - AD- Bangalore

Company Description

WNS, part of Capgemini, is an Agentic AI-powered leader in intelligent operations and transformation, serving more than 700 clients across 10 industries, including Banking and Financial Services, Healthcare, Insurance, Shipping and Logistics, and Travel and Hospitality. We bring together deep domain excellence – WNS’ core differentiator – with AI-powered platforms and analytics to help businesses innovate, scale, adapt and build resilience in a world defined by disruption.Our purpose is clear: to enable lasting business value by designing intelligent, human-led solutions that deliver sustainable outcomes and a differentiated impact. With three global headquarters across four continents, operations in 13 countries, 65 delivery centers and more than 66,000 employees, WNS combines scale, expertise and execution to create meaningful, measurable impact.

Job Description

Objective of the Role

The Principal Data Scientist is a key leader in the Data Science team, responsible for designing and delivering complex AI and data science initiatives that drive significant business value.

 This role leverages advanced analytics, cognitive computing, and AI to support strategic objectives, enhance customer experience, improve operational efficiency, and enable business growth through data-driven solutions.

As a technical leader, the role combines hands-on delivery with leadership responsibilities, including guiding small onshore and offshore teams.

 The scope includes rapid prototyping, piloting AI solutions, scaling production-ready models, and embedding industrialized data science assets into core business processes.

The role also drives innovation in advanced text analytics and Generative AI (GenAI) to enhance automation and customer experience.

The Principal Data Scientist is instrumental in shaping the organization’s AI and data science strategy by developing, implementing, and governing scalable analytical solutions that solve complex business problems and deliver measurable outcomes.

Key Behaviours

Provides clear direction, sets expectations, makes informed decisions, and holds teams accountable for achieving outcomes.

Demonstrates honesty, integrity, fairness, and sound risk management while empowering teams to deliver results.

Acts in the best interests of customers, champions innovation and digital transformation, promotes flexible ways of working, fosters collaboration and inclusivity, and creates an environment that prioritizes safety, wellbeing, diversity, and continuous learning.

Key Accountabilities

Leads high-impact AI and data science initiatives, including intelligent automation and AI-enabled decision support.

Develops AI-driven customer engagement solutions that improve customer interactions across products and channels.

Identifies and applies emerging AI technologies while providing technical leadership and mentoring to data science teams.

Partners with senior stakeholders to translate business challenges into analytical solutions that improve revenue, efficiency, and customer satisfaction.

Promotes innovation, communicates complex technical concepts to business leaders, and demonstrates the value of AI through prototypes, presentations, and successful business outcomes.

Contributes to the continuous enhancement of AI platforms, tools, methodologies, governance frameworks, and ethical AI practices to ensure high-quality, scalable solutions.

Key Stakeholder Relationships

Builds trusted relationships with internal business leaders and stakeholders, providing strategic guidance on AI and data science capabilities to support business objectives.

Maintains strong external networks within the broader Data Science and AI community to stay informed about emerging trends, technologies, and best practices.

Experience (minimum type and level of experience required to perform the role) We are looking for 8+ years practising in Data Science, AI or closely related disciplines

Key Capabilities / Technical Competencies

The ideal candidate possesses deep expertise in Data Science, Artificial Intelligence, and Machine Learning, with proven experience delivering value-driven analytical solutions, preferably within the financial services domain.

 They have a strong understanding of current and emerging AI techniques, enabling them to distinguish practical, high-impact solutions from industry hype.

The role requires excellence in end-to-end data science solution design, combining technical expertise with human-centered design principles to solve complex business problems from first principles.

Strong knowledge of business data ecosystems, advanced analytics, machine learning algorithms, AI system design, and modern data science platforms is essential.

Experience with scalable data engineering practices, distributed computing, Spark, and machine learning engineering is highly valued.

The successful candidate is outcome-oriented, focusing on delivering measurable business value while balancing technical excellence with practical execution, timelines, and budget constraints.

They demonstrate a strong work ethic, take initiative, and proactively drive projects to completion while fostering a collaborative and high-performing team environment.

A curious and research-driven mindset is critical, with a passion for exploring new technologies, staying current with AI advancements, and contributing thought leadership through research papers, whitepapers, or technical publications. Excellent communication and presentation skills are essential, with the ability to explain complex analytical concepts to both technical and non-technical audiences.

The candidate should be comfortable working across a range of delivery models, from rapid innovation and experimentation to structured, governed project implementation.

Qualifications

Qualifications

Mandatory: Bachelor Degree in technical subject area (data science, machine learning, AI, statistics, actuarial, engineering, maths etc.).

Post graduate degree in relevant statistical or data science or AI related area is highly desired. If no post-grad qualification then actual domain skills needs to place the individual at a similar capability level to suitably experienced Masters or PhD degree candidate with appropriate industry profile.

Membership of relevant professional body (IAPA, IAAust etc) is desirable.

Experience Level

Senior Level