Ganesh Naik

About Ganesh Naik

Hi there! Welcome to my LinkedIn profile. I’m a data-driven analyst with 3 years of experience turning complex data into actionable insights across the finance and healthcare industries. I specialize in predictive modeling, fraud analytics, credit risk, and interactive dashboards, using tools like Python, SQL, Power BI, and PySpark to support smart, data-informed decisions. I lead analytics projects end-to-end—from building ETL pipelines and developing statistical models to delivering clear, stakeholder-ready visualizations. My work has contributed to improvements in risk mitigation, customer retention, and operational efficiency. I’m passionate about bridging the gap between data and business strategy through automation, storytelling, and results-oriented analysis. Core strengths: Predictive analytics | BI dashboarding | A/B testing | Fraud detection | Credit risk modeling | Stakeholder collaboration Let’s connect and explore how data can drive better decisions.

Experience

1. Data Analyst

   Capital One Financial Services

   Jun 2024 — Present · US

   Managed and processed 30M+ rows of financial data from diverse sources (internal databases, Experian, Equifax, Salesforce), building automated ETL pipelines using SQL, Python, and AWS Glue that increased data availability. Built and deployed predictive models (logistic regression, XGBoost) for customer default risk with 92.3% accuracy, helping reduce credit loss by $8.4M annually and optimizing underwriting strategies. Performed statistical analysis and clustering (K-means, ARIMA) to identify customer segments and transaction trends, enabling targeted marketing campaigns that improved engagement by 18.7%. Created and maintained 5+ interactive dashboards in Tableau and Power BI, tracking KPIs such as delinquency rate, churn risk, and utilization; dashboards supported real-time decisions by risk and finance teams. Defined and engineered new variables (e.g, avg. transaction lag, CLTV, debt-to-income ratio) for modeling, increasing model precision and collaborated with product and risk teams to implement them into production. Led migration of reporting infrastructure to a Redshift + dbt stack, cutting report time from 6 hours to under 30 minutes, and developed anomaly detection systems to reduce fraud investigation time.

Education

• Savitribai Phule Pune University

  Bachelor of Technology - B.Tech

  2019 — 2023

• Binghamton University

  Master of Science - MS

  2023 — 2025