Senior Data Science Analyst at Global Payments in PUNE, , INDIA

Job description

• Deploys data-driven exploratory analysis as well as predictive models to solve business problems across financial services industry, particularly in the area of Originations, Risk and Fraud, Digital engagement, Payments and Customer Management. Designs and analyzes experiments to test new product ideas and convert the results into actionable product recommendations. Leads Analytics Model development, validation and maintenance. Assists with data collection, cleaning, visualization, model building, training, testing, and presentations to build analytics capability and drive efficiencies in business areas across TSYS.

Responsibilities

• Design and build statistical/ML models (regression, classification, and related techniques) to replace manual analysis and surface predictive insight.
• Engineer features and validate models with discipline — testing robustness, assessing performance, and choosing methods that fit the business problem.
• Rebuild report and analytical logic as SQL transformations in cloud data warehouses (e.g., Snowflake/BigQuery/Redshift/Synapse), working comfortably across large, unfamiliar schemas
• Validate parity vs. legacy outputs; reconcile discrepancies and document assumptions
• Translate model outputs into business-readable insights, visuals, and clear recommendations for stakeholders.
• Use AI tools to speed analysis and drafting while maintaining accuracy and controls

Requirements

• Must-Have
• BS in Statistical Background, Computer Science, Information Technology, Business/Management Information Systems, or related field
• 5-8 years of relevant experience, typically
• Python: hands-on proficiency with pandas, scikit-learn, and statsmodels for data preparation, modeling, and analysis
• SQL: proficient working with large datasets; able to write complex queries and move comfortably across unfamiliar schemas; Snowflake experience is a plus
• Statistical modeling: strong foundation in regression, classification, and hypothesis testing; able to choose methods that fit the business problem
• Feature engineering and model validation: able to create useful predictors, test model robustness, and assess performance with discipline
• Data visualization: able to translate model outputs into business-readable insights, visuals, and clear recommendations
• Comfort with fragmented, incomplete data: able to navigate multiple disconnected source systems, reconcile inconsistencies, and piece together a coherent picture without hand-holding
• AI-first mindset: daily user of AI tools (Copilot/ChatGPT/Claude or similar) to speed analysis and drafting; already works this way (not "will learn")
• Attention to detail: finance-grade accuracy; output feeds executive and regulatory reporting
• Background on Credit Risk , Lending
• Snowflake or any cloud data warehouse (BigQuery, Redshift)
• Experience in financial services, payments, or fintech
• Python or basic scripting
• Cross-domain data analysis: exposure to working with data from more than one business function or system; comfortable joining datasets that weren’t designed to be joined

Skills

Required

• Feature engineering and model validation
• Data visualization
• Comfort with fragmented, incomplete data
• Attention to detail

Preferred

• Background on Credit Risk, Lending
• Python or basic scripting
• Cross-domain data analysis