Actuarial - Data Science at WNS Global Services

Bangalore, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

22 Jul, 26

Salary

0.0

Posted On

23 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Pandas, Scikit-learn, Numpy, XGBoost, SHAP, Machine learning, Data preparation, Feature engineering, Model validation, London Market insurance, Risk modeling, Data analysis, Stakeholder management, Technical communication

Industry

Business Consulting and Services

Description

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 OverviewWe are seeking an experienced London Market Data Scientist to support the delivery of our tiering enhancement project. The role focuses on applying advanced machine learning techniques to classify risks across all London Market lines of business into low, medium, and high tiers.The contractor will enhance existing models using external and internal data, build robust Python pipelines and produce explainable outputs that underwriters can trust and adopt.Key ResponsibilitiesEnhance, train, validate, and optimise tiering models for London Market insurance classes using machine learning techniques such as XG BoostEnhance the end-to-end Tiering workflow process: feature engineering, data preparation, model selection, quality assurance and validationImplement scalable, reproducible modelling pipelines in PythonProduce clear, intuitive model explanations, narratives and visuals to underwriters and senior stakeholders for buy-inAddress any concerns around interpretability, bias and anomalies in resultsPlay a critical role in the technical rollout of tiering models across all London Market linesDeliver against tight timelines with strong prioritisation and structured executionCollaborate with data science, pricing and underwriting colleaguesEnsure high quality, reproducible and well-documented code. Required Skills & ExperienceStrong proficiency in Python (pandas, scikit learn, numpy, xgboost, SHAP or similar)Proven experience building and understanding machine learning classification models in the London Market insurance context Strong understanding of London Market pricing, risk drivers and line-of-business nuances Familiarity with sparse and volatile London Market data Proven ability to influence underwriters and gain buy-in for model adoptionExcellent communication skills, able to translate technical details into commercially meaningful insightsTrack record of delivering high-quality work under tight deadlinesCommercially minded, able to balance analytical rigour with practical business needsStrong analytical and problem-solving skills with the ability to challenge assumptions constructively and identify issues Experience in building validation checks and controls Self-sufficient, proactive and able to drive tasks independently Qualifications Graduate

Responsibilities

The role involves enhancing and optimizing machine learning tiering models for London Market insurance classes using Python. You will also be responsible for implementing scalable modeling pipelines and presenting explainable insights to underwriters and senior stakeholders.