I help small businesses and political campaigns turn messy spreadsheets, scattered reports, and untapped data into clear dashboards, automated workflows, and actionable strategy.
Four core services, all built around the same idea: your data should be working for you, not the other way around.
Replace the weekly scramble of pulling numbers from five different places. I build clean, automated dashboards and reports that update themselves — so you always know where things stand.
Got years of messy spreadsheets, duplicate records, or data you've never been able to make sense of? I'll clean it, structure it, and surface the patterns that matter for your decisions.
The information you need often exists online — in PDFs, government sites, or competitor pages — but there's no download button. I build custom scrapers that collect and structure it for you automatically.
Voter file analysis, precinct-level targeting, historical performance modeling, and field strategy recommendations. Data-driven campaigns win — I help you build the infrastructure for it.
Need to know what's actually driving your results — not just what correlates? I build Monte Carlo simulations to stress-test assumptions and use causal inference methods to isolate real effects from noise, so you can make decisions based on evidence, not guesswork.
A few examples of real projects and their outcomes.
Analyzed a 600,000-row Illinois voter file to compute precinct-level deviation from Democratic baseline. Built field targeting recommendations from the findings.
Built Excel/VBA automations and R-based reporting infrastructure to replace manual daily report prep. Created dashboards to expand data access across the organization.
Scraped and standardized presidential primary election data from 100+ state-party PDF documents into a single, clean, reproducible dataset for ongoing academic research.
Built a Python scraper to extract data from 250 PDF crime logs, then analyzed and visualized findings in an interactive R-based map for a university newsroom.
Designed a 700-iteration Monte Carlo simulation in R to evaluate how ANOVA holds up under varying effect sizes and non-normal error distributions. Analyzed Type I error rates and statistical power across conditions.
Analyzed 484,630 village-level observations using log-linear regression with interaction terms to isolate the causal effect of electricity access on consumption, controlling for poverty and gender.
I'm Devin Oommen — a data analyst based in the Chicago area. I graduated from Northern Illinois University in 2025 with honors in Political Science and a concentration in data science, including coursework in econometrics and computational statistics.
My background spans political data, academic research, journalism, and business analytics. I've worked with 600k-row voter files, built scraping pipelines for university researchers, automated reporting for a healthcare organization, designed Monte Carlo simulations to test statistical methods, and used causal inference to estimate policy effects across nearly half a million observations.
I'm especially interested in working with small businesses that know they're sitting on useful data but don't have the time or tools to do anything with it — and with political campaigns and organizations that want to make sharper, evidence-based decisions.
MPSA Conference Presenter · ICPA News Story of the Year · ICCJA Reporter of the Year · Peters Scholarship for Public Service · Mortar Board Honor Society
Free 15-minute consultation. No pitch, no pressure — just an honest look at whether I can help.
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