Data Scientist

Global Payment Holding Company; Pune District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Global Payments in Pune, India is seeking a Data Scientist to join the Customer Data & Portfolio Analytics team. You will develop and deploy advanced analytics, ML models, and AI solutions that drive customer growth, retention, and portfolio optimization at scale.

You will own end-to-end model lifecycle—from problem framing and EDA to feature engineering, validation, monitoring, and deployment—collaborating with cross-functional teams to operationalize data assets and model features.

Qualifications

• 4-5 years of experience in Data Science, ML, Analytics, or related fields.
• Strong proficiency in Python and SQL.
• Proven experience deploying production-grade ML models.
• Experience with customer analytics, portfolio management, or CRM analytics.
• Experience building predictive analytics solutions (attrition, segmentation, forecasting).
• Strong understanding of ML, statistics, feature engineering, and model validation.
• Experience with Databricks, Snowflake, SageMaker or similar cloud environments.
• Familiarity with MLOps including version control and monitoring.
• Exposure to GenAI (LLMs, NLP) to solve business problems.
• Experience with large-scale customer/transaction datasets.
• Desirable: experimentation, A/B testing, uplift analysis.
• Strong stakeholder communication and problem-solving skills.
• Financial services/FinTech domain experience preferred.

Responsibilities

• Independently design, train, and deploy ML models in production.
• Lead end-to-end model lifecycle from problem framing to deployment.
• Develop predictive models for attrition, cross-sell, segmentation, and LTV.
• Design AI-powered solutions using GenAI, NLP, and call listening for insights.
• Collaborate with Portfolio Management, Product, Sales, Marketing, and Data Engineering.
• Translate business challenges into scalable data pipelines and tools.
• Monitor model performance and retrain using production data.
• Communicate insights and outcomes to leadership to show impact.
• Evaluate emerging AI technologies and establish scalable practices.
• Align with global stakeholders for successful implementation.

Skills

Python

SQL

Production ML

Customer analytics

A/B testing

Stakeholder management

GenAI/NLP

MLOps

Cloud platforms

Education

Bachelor's degree in CS/Math/Statistics or related field

Tools

Databricks

Snowflake

AWS SageMaker

Job description

Ready to take your career global? Make your mark at one of the biggest names in payments. We're looking for a Data Scientist to join our ever-evolving Customer Data & Portfolio Analytics organization. In this role, you will help develop and deploy advanced analytics, machine learning, and AI solutions that drive customer growth, retention, and portfolio optimization at scale, helping to shape the future of Global Payments.

What you'll own

• 4-5 years of experience in Data Science, Machine Learning, Advanced Analytics, or related fields.
• Strong proficiency in Python and SQL.
• Proven experience developing, deploying, and maintaining production-grade machine learning models in production environments.
• 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.
• Strong understanding of machine learning, statistical modeling, feature engineering, and model validation.
• Experience with modern data science platforms such as Databricks, Snowflake, AWS SageMaker, or similar cloud environments.
• Familiarity with MLOps practices including version control, model monitoring, and deployment processes.
• Experience or exposure to AI/GenAI solutions using LLMs, NLP, or related technologies to solve complex 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 highly desirable.
• Strong stakeholder management , communication, and problem-solving skills with the ability to translate business challenges into analytical approaches.
• Financial Services, Payments, FinTech, Banking , or related industry experience is preferred.

What you'll bring

• Independently design, train, tune, evaluate, deploy, and maintain machine learning models in production environments.
• Lead the end-to-end lifecycle of machine learning models including problem definition, exploratory data analysis (EDA), feature engineering, validation, 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.
• Design and deploy AI-powered solutions leveraging GenAI, LLMs, NLP, and call listening/conversational intelligence to identify customer sentiment, churn risk indicators, and growth opportunities.
• Partner closely with cross-functional teams including Portfolio Management & Customer Success, Product, Sales, Marketing, Finance, BI, Data Engineering, and MLOps to source, validate, engineer, and operationalize data assets and model features.
• Translate complex business challenges into scalable analytical solutions, data pipelines, and decision-support tools that enable proactive portfolio management.
• Drive model performance monitoring , retraining, and continuous improvement using production data and business outcomes while aligning with enterprise data governance standards.
• Communicate actionable insights, recommendations, and model outcomes clearly to business and senior leadership teams to measure, track, and quantify business impact.
• Evaluate emerging AI technologies and establish scalable best practices across analytics use cases.
• Drive alignment across global stakeholders to ensure successful implementation, model adoption, and alignment with strategic objectives.