Suraj Sagar Chathiri Portfolio

Technical Skills

Python, SQL, Visual Studio Code, PyCharm

Advance Excel (VBA, Lookups, Macro, Pivot Table, Index), Tableau, SPSS, Informatica, Power BI, Oracle SAP, Salesforce.

Regression, Classification, Decision Trees, KNN, Naïve Bayes, K-means, Random Forest, Hypothesis Testing, A/B Testing, ANOVA.

MySQL, AWS (EC2, S3, Amazon Redshift), Azure (Data Lake, Data Factory)

Scikit‑learn, Matplotlib, Pandas, NumPy, Seaborn, Statsmodels

Jun 2024 – Present | Irving, TX

When I arrived at Caterpillar Financials in June 2024, the Teradata exports were a manual headache—so I crafted Python scripts that now deliver clean CSVs at the click of a button, freeing analysts to focus on insights instead of data wrangling.
With that friction removed, I turned to forecasting: leveraging Python and R, I built regression models that predicted equipment leasing patterns so accurately we slashed excess inventory by 18%.
To ensure every team had reliable data, I architected scalable ETL pipelines using Informatica, Talend, and Azure Databricks—transforming raw feeds into polished datasets ready for analysis.
Armed with trusted data, I designed interactive Tableau and Power BI dashboards, complete with custom filters and parameters, driving a 30% jump in stakeholder satisfaction and dramatically cutting report turnaround times.
Finally, I wove ML‑driven classification models and advanced R forecasting into our workflows, boosting overall efficiency by 35% and setting the stage for truly data‑driven decision making.

Jan 2022 – Oct 2023 | India

When I joined Bajaj Finance in January 2022, stakeholders were drowning in static reports—so I crafted dynamic Power BI and Excel dashboards that delivered real‑time financial insights, boosting satisfaction by 30%.
As demand for faster data grew, I overhauled our ETL process with Informatica and Talend, automating lookups and mappings to eliminate manual bottlenecks and accelerate data availability.
To turn raw numbers into foresight, I used R to perform variance analysis and forecasting—reducing report generation time by 35% and giving teams the confidence to plan ahead.
Seeing the power of predictive analytics, I developed classification models that flagged high‑risk loan accounts before defaults occurred, enhancing our risk management framework.
Finally, I integrated machine‑learning outputs into our dashboards—enabling executives to interactively explore risk scores and trend projections, and cementing data‑driven decision making across the organization.