Senior Data Scientist

Worldpay; Pune District; On-site; INR 2,400,000 - 4,000,000; Full time

Job summary

Worldpay seeks a hands-on Senior Data Scientist to develop and deploy analytics, ML, and AI solutions at scale for global SMB markets, driving growth, retention, and portfolio optimisation.

Operate within Customer Data & Portfolio Analytics, leading end-to-end model development and deployment across lifecycle use cases, including attrition prevention and cross-sell optimization, with measurable business impact.

Qualifications

• 4-5+ years in Data Science, ML, or advanced analytics.
• Experience deploying production-grade ML models.
• Experience in customer analytics, portfolio management, or CRM analytics.
• Experience with AI/GenAI, LLMs, NLP for business problems.
• Strong stakeholder management and communication skills.
• Proven ability to translate problems into analytical solutions.

Responsibilities

• Lead end-to-end lifecycle of ML models: problem definition, EDA, feature engineering, training, validation, deployment, monitoring, and optimization.
• Develop predictive models for attrition, cross-sell, dormancy, Next Best Action, and customer value.
• Design scalable data pipelines and productionize analytics solutions.
• Drive model performance monitoring, retraining, and impact measurement.
• Collaborate with Portfolio Management, Data Eng, and BI teams to operationalize features.
• Communicate insights and model outcomes to senior leadership.

Skills

Python

SQL

Feature Engineering

MLOps

Big data

Tools

Databricks

Snowflake

AWS SageMaker

Job description

SENIOR DATA SCIENTIST PORTFOLIO ANALYTICS, Global SMB

Position Summary

We are seeking a hands-on Senior Data Scientist to develop and deploy advanced analytics, machine learning, and AI solutions that drive customer growth, retention, and portfolio optimisation at scale. This role offers the opportunity to solve complex business problems, influence strategic decisions, and build intelligence products used across global SMB markets.

Operating within the Customer Data & Portfolio Analytics organization, this role partners with business and technology teams across markets to deliver scalable analytical products and predictive models. The individual will lead end-to-end model development, deployment, and optimization across customer lifecycle use cases including attrition prevention, cross-sell and upsell optimization, Next Best Action recommendations, Customer Retention tools, dormancy prediction, customer segmentation, customer lifetime value optimisation, and conversational intelligence.

This is a highly visible role that combines data science, AI innovation, and business partnership to deliver measurable commercial impact and influence strategic decision-making.

Key Responsibilities

Model Development & Deployment

• Lead the end-to-end lifecycle of machine learning models including problem definition, exploratory data analysis (EDA), feature engineering, model development, validation, deployment, monitoring, and optimization.
• Develop predictive models for attrition, cross-sell propensity, dormancy prediction, Next Best Action recommendations, portfolio compression, customer segmentation, and customer value optimization.
• Independently design, train, tune, evaluate, deploy, and maintain machine learning models in production environments.
• Design scalable analytical solutions and data pipelines that support model development and deployment.
• Drive model performance monitoring, retraining, and continuous improvement using production data and business outcomes.
• Ensure models are explainable, scalable, and aligned with enterprise data governance standards.

AI & Customer Intelligence

• Design and deploy AI-powered solutions leveraging GenAI, LLMs, NLP, and related AI technologies.
• Develop call listening and conversational intelligence solutions to identify customer sentiment, churn risk indicators, emerging customer needs, and growth opportunities.
• Build AI-assisted workflows that improve customer engagement, portfolio management, and decision-making.
• Evaluate emerging AI technologies and establish scalable best practices across analytics use cases.

Analytics & Business Impact

• Translate complex business challenges into scalable analytics and machine learning solutions.
• Partner with stakeholders to identify opportunities that improve customer retention, engagement, product adoption, and revenue growth.
• Develop analytical products and decision-support tools that enable proactive portfolio management.
• Perform exploratory data analysis and hypothesis testing to uncover insights and support model development.
• Communicate actionable insights, recommendations, and model outcomes to business and senior leadership teams.
• Measure, track, and quantify business impact from analytical initiatives.

Cross-Functional Collaboration

• Partner closely with Portfolio Management & Customer Success, Product, Sales, Marketing, Finance & BI teams.
• Partner with Data Engineering and BI teams to identify, source, validate, engineer, and operationalize data assets and model features.
• Collaborate with Portfolio Analytics leadership and business leaders to prioritize use cases, drive model adoption, and ensure alignment with strategic objectives.
• Work with Data Engineering and MLOps teams to productionize and scale analytical solutions.
• Drive alignment across global stakeholders to ensure successful implementation and adoption.

Experience

• 4-5 years of experience in Data Science, Machine Learning, Advanced Analytics, or related fields.
• Proven experience developing, deploying, and maintaining production-grade machine learning models.
• Experience in customer analytics, portfolio management, commercial analytics, CRM analytics, marketing analytics, or customer lifecycle management.
• Experience building predictive analytics solutions such as attrition/churn models, propensity models, customer segmentation, recommendation systems, forecasting solutions, customer lifetime value models, or Next Best Action frameworks.
• Experience or exposure to AI/GenAI solutions using LLMs, NLP, or related technologies to solve business problems.
• Experience working with large-scale customer, transaction, sales, or operational datasets.
• Experience with experimentation, A/B testing, uplift analysis, or impact measurement is desirable.
• Proven demonstration of analytical thinking and problem-solving skills in data-rich environments.
• Financial Services, Payments, FinTech, Banking, or related industry experience preferred.

Technical & Domain Expertise

• Strong proficiency in Python and SQL.
• Strong understanding of machine learning, statistical modelling, feature engineering, and model validation.
• Experience applying predictive analytics techniques to customer, commercial, or portfolio management use cases.
• Experience with modern data science platforms such as Databricks, Snowflake, AWS SageMaker, or similar environments.
• Familiarity with MLOps practices including version control, model monitoring, and deployment processes.
• Exposure to AI/GenAI technologies, LLMs, NLP, or related frameworks.
• Ability to translate business problems into analytical approaches and actionable recommendations.
• Strong stakeholder management, communication, and problem-solving skills.

Why Join Us

• Own and deliver high-impact machine learning and AI solutions used by global business teams.
• Influence customer growth, retention, portfolio optimization, and strategic decision-making across international markets.
• Partner directly with business leaders, technology teams, and analytics leaders to solve complex commercial challenges.
• Work with modern cloud, AI, and data science platforms including Databricks, Snowflake, AWS, GenAI, and LLM technologies.
• Help shape the future of AI-powered customer intelligence and portfolio analytics capabilities.