Working in Technology (WiT) Data Science - Summer 2027

William Blair•Chicago, IL

About The Position

William Blair is seeking graduate students to join our innovative technology team for a 12-week internship program during the summer of 2027. The program is designed to promote technology careers among high performing individuals from underrepresented backgrounds in the technology field, including but not limited to women currently enrolled, or intending to enroll, in a technology-related academic program. Selected participants will have a unique opportunity to explore areas of statistical analysis, machine learning, deep learning, generative AI, data science, data visualization, and data engineering while supporting mission-critical business use cases. The summer program also includes technical training, mentorship, community service, and social events.

Requirements

Currently pursuing a Master's Degree in Statistics, Business Analytics, Data Science, Computer Science, or a related quantitative field, with anticipated graduation between December 2027 and June 2028
Experience using Python and/or R to manipulate data and derive insights from large datasets
Working SQL knowledge and experience with relational and NoSQL databases
Knowledge and experience of statistical analysis, data mining, and predictive modeling techniques
Experience with machine learning, deep learning, and NLP concepts and frameworks
Familiarity with social network analysis concepts
Experience with data discovery, analytics, and visualization tools
Experience or academic exposure to working with structured and unstructured data

Nice To Haves

Experience working with large and complex datasets through coursework, research, or projects
Exposure to cloud platforms (Azure/AWS), big data technologies (Spark, Kafka), or workflow orchestration tools (Airflow, Azure Data Factory)
Familiarity with Python data science and machine learning libraries such as Pandas, Scikit-learn, and PyTorch
Coursework, research, projects, or internship experience involving machine learning, NLP, generative AI, or large language models
Exposure to LLM APIs and frameworks such as LangChain, LlamaIndex, or Hugging Face
Exposure to AI engineering and MLOps concepts, including containers, experiment tracking, and model deployment
Understanding of Responsible AI principles, including data privacy, transparency, fairness, and human oversight

Responsibilities

Work on data science, machine learning, generative AI, and data engineering projects in Azure cloud and Spark environments
Assemble large, complex datasets that meet functional and non-functional business requirements
Analyze complex data sources to derive actionable business insights
Develop, test, and deploy predictive models using machine learning algorithms and translate results into business recommendations
Support AI use cases involving retrieval-augmented generation (RAG), document summarization, and information extraction
Assist with prompt engineering and benchmark dataset development for AI applications
Contribute to AI workflow automation and agent-based solutions
Assess AI model performance, bias, and failure modes and document findings
Prepare and validate data for AI applications, including embeddings, vector databases, and data quality processes
Communicate results through reports, dashboards, presentations, and demonstrations
Collaborate with business and technical stakeholders to identify and prioritize requirements
Work with data and analytics experts to continuously improve enterprise data systems