Data Science Intern: Predictive Lead Scoring & B2B Data Pipelines (USA, Remote)

Remote · Part-time (15–20 hrs/week) · 6 or 12 month commitment · Start date: Immediate / rolling

About Marketeq
Marketeq is an early-stage software startup building a proprietary SaaS platform. Specifics about the product are shared with candidates later in the process, once there's mutual interest and an NDA in place.

What we can tell you up front is how we build. AI does most of the heavy coding, so our engineers spend their time on the things that actually determine quality: writing sharp requirements, directing AI tools to build to those requirements, and rigorously reviewing and testing what comes back. We're looking for interns who thrive in exactly that model.

The Role
We're building a revenue intelligence system that discovers, enriches, and scores B2B leads, and predicts which services each company is most likely to need. You'll work directly with leadership on open-ended research problems rather than a fixed ticket queue. You'll own experiments from data source to deployed model.

This role suits Master's students in Data Science, Analytics, or related fields who want real-world experience applying ML to messy business data.

What You'll Do

• Research and evaluate external B2B data sources and APIs for company discovery and enrichment
• Build enrichment pipelines that combine public web data and third-party providers
• Develop predictive lead scoring models that estimate which companies need which services
• Use embeddings and semantic search to match companies with relevant service offerings
• Automate collection, enrichment, and scoring workflows
• Turn model outputs into dashboards and data-driven landing pages for the sales team
• Run experiments comparing lead discovery strategies, and document what works

Tech Stack
Python · SQL/PostgreSQL · Pinecone (vector DB) · n8n · Node.js/TypeScript microservices · third-party data APIs · AI-assisted development tools

What You'll Get

• Weekly 1:1 mentorship
• Ownership of portfolio-ready projects in predictive modeling and data engineering
• Exposure to the full lifecycle: research, modeling, deployment, business impact
• Academic credit eligibility

You Might Be a Fit If You

• Are pursuing (or recently completed) a Master's in Data Science, Analytics, or a related field
• Are comfortable with Python for data analysis and ML (pandas, scikit-learn, etc.)
• Enjoy working with incomplete, messy real-world data
• Like researching new tools and data sources on your own
• Are curious about how data science drives sales and marketing decisions

Nice to Have

• Experience with embeddings, vector databases, or NLP
• Familiarity with APIs, automation tools, or JavaScript/TypeScript

How to Apply
Send your resume and a short note about a data project you're proud of. Include a GitHub link if you have one. We review applications on a rolling basis and aim to start immediately.