Summer 2027 Data Scientist Intern – Data + AI
Location: New York, NY | Hybrid
Program Duration: Summer 2027 (10-12 Weeks) (30 hr per week commitment) Looking for applicants anticipated to graduate between December 2027 & May 2028 About M ScienceM Science helps institutional investors see what is changing across businesses, industries, and consumer behavior—and understand why it matters. Our team turns differentiated data into intelligence that helps investors find signals, monitor investment ideas, and build conviction. M Science gives clients the flexibility to use that intelligence in the way that fits their process: read our research, explore the underlying data through our Unified Data Model, or bring it into AI tools and internal systems through our MCP Server.
The Opportunity:
M Science is seeking a motivated and curious Data Scientist Intern to join our Summer 2027 Internship Program. This internship is designed for students passionate about differentiated data, AI, and data science/engineering. You will gain hands-on experience building and testing data pipelines, developing AI/ML tools, and applying statistical models to real-world differentiated datasets. Interns at M Science work alongside experienced data scientists and engineers to support impactful research used by hedge funds, asset managers, and other institutional clients.
Key Responsibilities:
Assist in building and optimizing data pipelines using Databricks, and PySpark.
Support the development of AI/ML workflows and agents for automated insights, data analysis, and forecasting.
Contribute to data testing, validation, and quality assurance, including anomaly detection and error handling.
Process, cleanse, and verify the integrity of large and complex datasets.
Help evaluate new differentiated data assets for potential research and investment use.
Work with data scientists to implement and document unit-tested analytics tools and models.
Collaborate with cross-functional teams to support analytics, research, and client deliverables.
What You’ll Learn:
How to leverage differentiated data and AI tools in investment research and analytics.
Best practices in data engineering, testing, and pipeline optimization.
Application of Python, SQL, PySpark, and Databricks for large-scale data processing.
Fundamentals of statistical modeling and machine learning, including multivariate and ensemble methods.
Introduction to AI/LLM frameworks and agent-based workflows.
Preferred Qualifications:
Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field anticipated to graduate between December 2027 and May 2028.Strong programming skills in Python and SQL; experience with PySpark/Spark is a plus.
Strong background in math and statistics.
Interest or exposure to machine learning, AI agents, or large language models.
Highly analytical, detail-oriented, and able to work independently and collaboratively.
Strong written and verbal communication skills.
Nice-to-Have Skills:
Experience with LLM orchestration frameworks like LangChain or LangGraph.
Familiarity with cloud platforms (AWS, Databricks) and distributed data processing.
Prior experience with data testing, anomaly detection, or object-oriented programming.
Why M Science?
Hands-on experience with real differentiated datasets and AI tools used in financial research.
Work alongside senior data scientists and engineers on impactful projects.
Hybrid work model offering both in-office collaboration and flexibility.
Exposure to cutting-edge analytics in finance, technology, and data science.
Hourly Rate: Up to $45/hr USD
