Christopher Beganski
Data & AI Consultant · Biopharma Analytics
Littleton, Massachusetts
Things I've built. Some are client work, some came out of the MIT program, and one started because I needed it myself. This page grows as I ship.
In the Wild
BGH Scout
By the time a role reaches the major job boards, it is usually already flooded with applicants. The people I met while job searching were not short on talent or effort — they were short on visibility into openings that had not yet surfaced.
So I built the thing that was missing: an automated job intelligence platform that scrapes employer career sites directly, every day, and puts new postings in front of people before the boards catch up. Ben Greene and Ty Holland joined to turn it into a real product.
- 400+ employer career sites scraped daily across 14 industries.
- 2,000+ new postings surfaced per day, delivered through a dedicated web application.
- Scaled to 120+ active users.
- Includes ways to identify and connect with people inside the hiring companies.
Medical Field Insight Classification & Recommendation Pipeline
A multi-stage system for a pharmaceutical client: an NLP classifier categorizes field observations against a Strategic Medical Plan, an LLM produces ranked and citation-backed recommended actions per objective, and a differential stage reports what changed since the previous cycle. In active production use.
MIT Applied AI & Data Science
Fourteen weeks through MIT Professional Education, completed January 2026. The projects below are the graded coursework; the full portfolio is published here.
Marketing Campaign Customer Segmentation
Analyzed customer data to understand buying behaviour and campaign response, grouping customers by income, spending patterns, family size and campaign acceptance using PCA and K-Means clustering. The resulting segments support more precise targeting and better campaign performance.
Potential Customer Prediction
Built classification models to predict which leads for an EdTech startup would convert to paid customers, using demographic detail and interaction behaviour. The work identified the factors driving conversion and produced lead profiles to focus marketing and sales effort.
FoodHub Delivery Data Analysis
Examined order cost, food preparation time, delivery time and customer ratings across weekdays and weekends for a New York food delivery service, surfacing where delivery speed and restaurant performance could be improved.
Get in Touch
If you have a problem that looks like one of these, I'd be glad to talk it through.