What are the responsibilities and job description for the Data Scientist - Geospatial and Remote Sensing (Summer Internship - Master's) position at Balyasny Asset Management?
Geospatial Data Science and Remote Sensing are key differentiators for BAM's Commodities strategy. The ability to leverage data effectively is increasingly central to our investment process. The Commodities Data Science team applies advanced analytics, machine learning, and AI to develop tools, signals, and decision-support capabilities that help global Investment Teams improve investment outcomes.
As a Geospatial Data Scientist Intern, you will complete a hands-on 10-week program focused on solving complex, real-world problems. You will gain firsthand experience applying data science and AI to support the commodities investment process. The internship begins with a training block covering the Commodities business and the investment process before you join the desk. You will also receive mentorship from senior team members, collaborate closely with peers, and build relationships across the broader intern cohort.
In this role, you will leverage BAM's alternative datasets to develop practical geospatial analytics and data products. Projects may include satellite and aerial imagery analysis, GPS mobility data, meteorological and hydrological modeling, crop-yield forecasting, and other spatial data science applications.
You will work across the end-to-end development process: translating investment-team needs into analytical solutions; preparing and integrating data; developing, validating, and improving statistical and machine-learning models; and communicating results through clear, usable outputs for investment professionals and senior stakeholders.
Responsibilities:
Qualifications:
Opportunities are available in our New York office.
As a Geospatial Data Scientist Intern, you will complete a hands-on 10-week program focused on solving complex, real-world problems. You will gain firsthand experience applying data science and AI to support the commodities investment process. The internship begins with a training block covering the Commodities business and the investment process before you join the desk. You will also receive mentorship from senior team members, collaborate closely with peers, and build relationships across the broader intern cohort.
In this role, you will leverage BAM's alternative datasets to develop practical geospatial analytics and data products. Projects may include satellite and aerial imagery analysis, GPS mobility data, meteorological and hydrological modeling, crop-yield forecasting, and other spatial data science applications.
You will work across the end-to-end development process: translating investment-team needs into analytical solutions; preparing and integrating data; developing, validating, and improving statistical and machine-learning models; and communicating results through clear, usable outputs for investment professionals and senior stakeholders.
Responsibilities:
- Applied Analytics: Use structured and alternative data to solve commercially relevant problems for Commodities Investment Teams
- Insights & Communication: Produce actionable insights through analysis, visualizations, dashboards, and concise written output to technical and non-technical audiences
- Model Development: Build, validate, and improve statistical and machine-learning models and signals
- Data Engineering: Develop reliable data pipelines and datasets for analytical and investment use cases
- Production Deployment: Deploy and maintain data products with cloud-native architecture in mind
- Teamwork & Mindset: Self-driven and results oriented. Perpetually curious while staying exceptionally organized and collaborative in a fast-paced, ambiguous environment
Qualifications:
- Master's student graduating between December 2027 and May 2028 who is pursuing a degree Computer Science, Mathematics, Statistics, Remote Sensing, Geography, Geomatics, Geographic Information Systems (GIS), Geospatial Data Science with a focus on Machine Learning and/or Generative AI techniques, or another related quantitative field.
- Exceptional analytical, data processing and programming skills (at a minimum, python, numpy, pandas, scikit-learn, matplotlib and SQL)
- Exceptional understanding of statistical and machine learning concepts
- Familiarity with ETL tools and managing code repositories (GitHub)
- Ability to clearly communicate complex and technical subject matters
- Results driven mindset, ability to work in an ambiguous environment, and work collaboratively within a team environment
- Experience with the following technologies is a plus: Apache Airflow, AWS, PostGIS, Spark, STAC, Databricks, Deep learning frameworks (e.g., Tensorflow, Keras and PyTorch)
- Prior experience working with alternative datasets is a strong plus.
Opportunities are available in our New York office.