M
Full Time
Any
Job detail
Predictive Analytics Intern
Marketeq Digital
Miami, Florida, United States
Job description
Remote Unpaid Internship 4 6 months hrs/week Masters Degree Required
For immediate hire only
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 Masters 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 Youll 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 Masters 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
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
For immediate hire only
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 Masters 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 Youll 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 Masters 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
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