Sudarshan Dasarathy

About Sudarshan Dasarathy

Senior Data Analyst with 5+ years of experience delivering end-to-end data solutions in Banking, fintech and data-intensive industries. Proven ability to convert complex datasets into strategic insights using SQL, Python, and Tableau, enabling fraud detection, compliance automation, and operational efficiency. Skilled in predictive modeling, statistical analysis, and feature engineering to support machine learning and AI-driven initiatives. Hands-on experience in building robust data pipelines, ensuring data quality and lineage, and optimizing data architectures. Strong collaborator with data engineering teams to enhance integration and cloud-native processing. Proficient in modern tech stacks including AWS, Snowflake, and MongoDB, with practical expertise in leveraging document databases to support scalable analytics and agile product development.

Experience

1. Associate Consultant

   Capgemini

   Aug 2021 — Present · NJ, US

   Financial ServicesLed the design and launch of an AI-powered Fraud Risk Scoring Model-as-a-Service (MaaS), significantly enhancing fraud detection across digital transaction types. This initiative resulted in a $4M annual reduction in fraud losses by boosting detection accuracy, lowering false positives, and enabling more efficient risk-based decisions. Engineered and integrated real-time data pipelines for over 170 fraud signals—spanning money movement, digital behavior, payments, and scams—by validating enterprise data warehouse sources and cross-referencing with Elasticsearch, reducing false positives and improving data accuracy. Developed and optimized complex SQL workflows to support ETL processes and schema optimization, utilizing Spark SQL, Redis, and NetApp S3. This effort cut data processing latency by 50%, enabling scalable processing of millions of daily transactions. Directed comprehensive data exploration, profiling, and feature engineering strategies using statistical methods to support AI model development. Collaborated closely with risk modeling and fraud strategy teams to align model outputs with business logic, resulting in a 28% boost in operational efficiency.Investment BankgSpearheaded cross-functional collaboration with data science, engineering, and business teams to conduct in-depth research and analysis that eliminated data silos, resulting in a 25% improvement in data accessibility and operational efficiency across departments.Performed deep analysis of large-scale Oracle relational databases to trace data lineage, classify data structures, and identify interdependencies—driving a data-driven strategy for key insight extraction and empowering third-party tools to address complex data integration challenges.• Designed structured data models to map application data flows, enhancing visibility into data relationships and creating retention strategies; projected insights via DDL scripts in Tableau for key decision-making

Education

• University of Mumbai

  Bachelor of Engineering, Computer Engineering

  2014 — 2018

• Stevens Institute of Technology

  Master of Science - MS, Management Information Systems, General

  2019 — 2021