Data Science Intern - Predictive Lead Intelligence (Remote, USA-Based)

Remote (US only) · Unpaid · 15–20 hrs/week · 4–6 months · Starting immediately
Build a real ML system from the ground up, working directly with leadership.

What you'll do

• Research and evaluate external B2B data sources, APIs, and datasets for lead discovery and enrichment
• Build enrichment pipelines that collect and clean company and decision-maker data
• Design and test predictive lead-scoring models using ML and statistical analysis
• Use embeddings and semantic search (e.g., Pinecone) to match companies with relevant consulting and digital services
• Automate collection, enrichment, and scoring workflows (n8n, microservices)
• Surface insights in dashboards and dynamic landing pages used by the sales team
• Run experiments comparing lead-discovery strategies to find what produces the best opportunities
• Document your work so it becomes reusable infrastructure

What you'll gain

• A production-grade, end-to-end ML project for your portfolio, from data acquisition to deployed scoring
• Direct mentorship and regular 1:1s with leadership
• Hands-on experience with modern AI-assisted development workflows
• Flexible hours that fit around your coursework

Tech stack
Python · SQL/PostgreSQL · Pinecone/vector databases · third-party data APIs · web scraping · n8n · Node.js/TypeScript · data visualization

You're a great fit if you:

• Are currently pursuing a Master's in Data Science, Data Analytics, or a related field
• Are based in the US and authorized to work in the US
• Communicate clearly and professionally in written and spoken English
• Are comfortable with Python for data analysis and machine learning
• Like working with messy, real-world data
• Enjoy research and figuring out problems without step-by-step instructions
• Are curious about how data science drives sales, marketing, and product decisions

Please note: This is not a summer internship. We're looking for someone who can start now and commit 15–20 hours per week for 4–6 months.