Data Science Intern | Fully Remote US

About the role

As a Data Science Intern, you will support ongoing research initiatives that keep our enterprise products and our clients' hiring processes on the absolute cutting edge and leverage machine learning to shape the future of hiring. Partnering closely with our world-class data scientists and cross-functional partners in Product Science and Product and Technology, you will have the opportunity to build, validate, and scale machine learning models and agentic workflows. You will also conduct applied research on systems and processes to continuously advance our natural language processing (NLP) and ethical AI capabilities.

Essential Duties And Responsibilities

• Applied Research & Modeling: Support ongoing research initiatives that keep us on the cutting edge, including in agentic workflow development
• Work with machine learning models to build products and conduct research
• Write code to develop prototypes and utilities to extend or improve our product suite
• Data Preparation & Quality: Drive the preparation of data used to train deep learning models and score unstructured data
• Assure quality of our software in terms of content, scoring, and functionality
• Performance modeling and data analysis

Qualifications

• Bachelor’s degree required, with a Master’s degree or PhD in progress in a quantitative/programming field (Computer Science, Statistics, Mathematics, Engineering, Psychology, Economics, Data Science, or related)
• Understanding of statistics, machine learning, and deep learning fundamentals
• Proficiency in Python, including common ML libraries (e.g., numpy, pandas, scikit-learn)
• Experience with version control (e.g., Git)
• Ability to analyze large datasets and draw meaningful, actionable insights
• Strong problem-solving skills — able to synthesize information from multiple sources to solve problems
• Team player with strong collaboration and communication skills; comfortable working closely with data scientists, engineers, and I/O Psychologists
• Self-motivated and able to work effectively with limited supervision; strong prioritization skills

Preferred

• Awareness of state of the art methods in machine learning, deep learning, NLP, and/or generative AI
• Exposure to large language models (LLMs) and generative AI (e.g., prompt engineering, fine-tuning, agentic design patterns, agentic evaluation methods)
• Familiarity with SQL and database querying
• Familiarity with cloud platforms (AWS preferred) for ML workflows