Thomas Nammi
@thomasnammi
I build SQL, Python, and dashboard analytics that improve forecasting, decisions, and operational performance.
United StatesWhat I'm looking for
At Capgemini, I build SQL and Python analytics workflows, dashboards, and machine learning models that support decisions across 100+ stores. My work improved decision-making efficiency by 25%, forecast accuracy by 18%, and report accuracy by reducing errors 40%.
I've also delivered data science work for Torqata, Mastercard, and Deloitte, using Redshift, PostgreSQL, dbt, AWS, Tableau, Power BI, Looker, and Redash. I automate data pipelines, analyze large transactional datasets, and turn demand, pricing, churn, and customer behavior signals into actionable business insights.
At Mastercard, I built a Random Forest churn model with an AUC of 0.86 and automated ingestion and transformation workflows that reduced manual effort by 80%.
My background also includes published research in machine learning, image processing, and medical imaging, with a paper presented at IEEE ICCSP and published on IEEE Xplore.
Experience
Work history, roles, and key accomplishments
Developed SQL queries and Python scripts to create interactive dashboards delivering insights across 100+ stores, improving decision-making efficiency by 25%. Built machine learning models and Python workflows integrated with SQL data pipelines to maintain customer segmentation dashboards, increasing campaign conversion rates by 15%.
Data Science Intern
Torqata
May 2023 - Oct 2023 (5 months)
Constructed complex SQL queries and Python scripts using Redshift and PostgreSQL to extract and transform datasets exceeding 100 million records, enabling demand forecasting models with 15% improved error rates. Developed interactive Tableau and Looker dashboards communicating regional demand trends and pricing insights.
Automated data ingestion and transformation workflows using SQL, Python, and dbt, reducing manual effort by 80% and accelerating report delivery cycles in AWS environments. Built Python machine learning models including Random Forest classifiers to predict customer churn with an AUC of 0.86.
Analyzed complex claims datasets using SQL and Python to perform exploratory data analyses, uncovering patterns that reduced manual review time by 30% and improving reporting accuracy. Developed interactive dashboards with SQL-driven data and visualization tools including Tableau and Power BI.
Education
Degrees, certifications, and relevant coursework
University of Rochester
Master of Science, Data Science
2022 - 2023
Grade: 3.8
Master of Science in Data Science with a CGPA of 3.8.
Availability
Location
United StatesAuthorized to work in
Portfolio
ieeexplore.ieee.org/document/8524438Job categories
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