Yogesh Suraparaju

Data Analyst with 5 years in telecom and IT consulting, known for turning messy usage, billing, and client data into numbers leadership actually trusts. At T-Mobile, that means digging through millions of subscriber records to explain why churn moves the way it does; at Capgemini, it meant building the dashboard a client account team opened every single Monday. SQL and Python are the everyday tools; Power BI and Tableau are how the findings reach people who don't want to read a query.

Over the past year I've added generative AI to that toolkit building RAG pipelines with LangChain and the OpenAI API, and evaluating LLM output against labeled data so automated summaries hold up under scrutiny, not just look polished in a demo.

AI Data Analyst at T-Mobile (2025-03 – Present)

• Analyzed call detail records and billing data across millions of subscribers with SQL and Python, uncovering churn drivers behind a 12 percent spike.
• Built and maintained ten Power BI dashboards tracking network performance and customer satisfaction, cutting weekly reporting time for regional managers by 35 percent.
• Introduced a GenAI workflow using prompt engineering and the OpenAI API to auto-summarize customer feedback, saving the CX team six hours weekly.
• Designed a RAG-based knowledge assistant over internal churn reports using LangChain and vector search, cutting analyst lookup time for prior findings by 30 percent.
• Partnered with network operations to validate incoming usage feeds against OSS records, reducing data quality tickets by 22 percent over two quarters.
• Presented churn and network-performance findings during Agile ceremonies, translating technical results into plain language that shaped two roadmap decisions this year.

Data Analyst at Capgemini (2021-02 – 2023-07)

• Designed and optimized complex SQL queries against large multi-client datasets spanning banking and retail accounts, improving reporting efficiency by 30 percent.
• Built interactive Tableau and Power BI dashboards for two client engagements, cutting reporting turnaround time by 25 percent during peak delivery periods.
• Cleaned and transformed messy client datasets using Python and Excel, improving data accuracy by 20 percent ahead of recurring monthly reporting deadlines.
• Gathered requirements directly from business stakeholders across two accounts, turning vague asks into clear analysis that lifted client satisfaction scores by 15 percent.
• Automated repetitive reporting tasks with custom Python scripts, cutting manual effort by 40 percent and freeing the team to focus on deeper analysis.
• Mentored two junior analysts on SQL fundamentals and dashboard design, shortening their onboarding ramp-up time by two weeks.
• Documented data definitions and query logic in a shared analytics wiki, cutting onboarding time for new consultants joining the reporting team.

Junior Data Analyst at eClerx (2020-06 – 2021-01)

• Processed and reconciled structured datasets in Excel and SQL for financial-services and cable clients, improving data accuracy by 15 percent.
• Built Tableau dashboards for internal teams each sprint, reducing report preparation time by 20 percent across three ongoing client projects.
• Performed data validation and quality checks using Excel and SQL, ensuring consistency across datasets feeding weekly client-facing reporting cycles.
• Generated insights from raw operational data through structured analysis, improving efficiency and cutting reporting errors by 12 percent.

Master of Science in Information Technology – Saint Francis College, Brooklyn, NY (2024-01 – 2025-08)