Data Scientist

Overview

Data Scientist role in the Customer Data & Portfolio Analytics organization, developing and deploying advanced analytics, machine learning and AI solutions to drive customer growth, retention and portfolio optimization.

What you'll do

• 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.

What you'll need

• 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.
• Strong stakeholder management, communication and problem-solving skills, with the ability to translate business challenges into analytical approaches.

Nice to have

• Experience with experimentation, A/B testing, uplift analysis or impact measurement.
• Financial Services, Payments, FinTech, Banking or related industry experience.

Details

• Location: Pune, India.