Data Science & Lead Intelligence Intern

Data Science & Lead Intelligence Intern at Marketeq Digital Remote | Unpaid Internship | 4–6 months | 15–20 hrs/week | Master’s Degree Required *THIS IS NOT A SUMMER INTERNSHIP — WE ARE LOOKING FOR IMMEDIATE HIRES * About the Internship We are building a data-driven revenue intelligence system designed to transform how B2B leads are discovered, enriched, scored, and converted into sales opportunities. This internship focuses on using data science, predictive analytics, automation, and microservices to create intelligent sales pipelines that identify high-value companies and predict the services they are most likely to need. Unlike traditional engineering internships where interns simply implement predefined requirements, this role is highly strategic and research-driven. Interns collaborate directly with leadership to experiment with creative ways of using external data sources to improve lead discovery, enrichment, and predictability. You will research external datasets, build enrichment pipelines, design predictive lead scoring models, and develop modular components that become part of a scalable marketing and revenue intelligence infrastructure. This role is ideal for Master’s students in Data Science or Data Analytics who want hands-on experience applying advanced data science techniques to real-world business problems like B2B lead generation, predictive targeting, and revenue intelligence systems. What You’ll Work On Research external B2B data sources, APIs, and datasets that can be used to discover and enrich potential client data. Design and build data enrichment pipelines that collect additional information about companies and decision-makers from sources such as websites, LinkedIn, and third-party APIs. Develop predictive lead scoring models that estimate which companies are most likely to need specific services. Apply machine learning and statistical analysis to identify patterns that indicate high-value B2B opportunities. Use semantic analysis and vector embeddings to match companies with relevant service offerings. Build modular components and microservices that automate the lead enrichment and scoring process. Create automation workflows that continuously collect, enrich, and evaluate lead data. Design systems that predict which IT consulting or digital services a company may be interested in based on its data profile. Build data-driven landing pages or dashboards that dynamically display insights generated from enriched datasets. Integrate live data insights into landing pages used to book strategy calls for the sales team. Experiment with different data acquisition strategies to determine which lead discovery methods produce the highest quality opportunities. Document all systems, pipelines, and models so they can become reusable components within a scalable marketing infrastructure. Technologies & Tools You May Work With Python for data analysis, machine learning, and predictive modeling SQL / PostgreSQL for data storage and querying Vector databases such as Pinecone for semantic matching APIs and third-party data providers for enrichment and lead discovery Web scraping and automated data extraction Automation frameworks such as n8n Microservices architecture using Node.js / TypeScript AI coding tools and modern AI-assisted development workflows Semantic search and embedding-based data matching Data visualization dashboards and dynamic landing page integrations Types of Problems You May Solve Predicting which companies are most likely to purchase specific services Identifying hidden B2B opportunities through external datasets Enriching incomplete company profiles with third-party data Building models that recommend the most relevant data sources for different industries Creating systems that automatically surface high-value leads to sales teams Matching company characteristics with appropriate consulting or technology solutions Who This Internship Is For This internship is ideal for graduate students who enjoy combining data science with strategy, automation, and product thinking. You may be a strong fit if you: Are pursuing a Master’s degree in Data Science, Data Analytics, or a related field Have experience using Python for data analysis or machine learning Enjoy working with messy real-world datasets and extracting useful insights Are comfortable researching APIs, data sources, and enrichment methods Are curious about applying data science to sales intelligence and marketing systems Enjoy building systems that automate decision-making and predictions Are interested in combining data science, marketing, product strategy, and automation Internship Details Remote internship 15–20 hours per week Duration: 4–6 months Unpaid internship designed for graduate-level learning and applied research experience Work directly with leadership on experimental revenue intelligence systems Opportunity to build portfolio-level projects involving predictive analytics and data pipelines