Overview
Full Time
Skills
Research
Profit And Loss
Artificial Intelligence
Training
Mentorship
Network
FOCUS
Analytics
Data Wrangling
Testing
Satellite
SAR
Meta-data Management
Image Processing
Forecasting
Research Design
POC
Partnership
Collaboration
Teamwork
Organized
Erdas Imagine
ENVI
Esri
CGI
Geospatial Analysis
Information Systems
Computer Science
Mathematics
Statistics
Data Science
Analytical Skill
Data Processing
Python
NumPy
Pandas
scikit-learn
matplotlib
SQL
Machine Learning (ML)
Extract
Transform
Load
Management
GitHub
Geographic Information System
QGIS
ArcGIS
Vector Databases
Apache Airflow
Amazon Web Services
Apache Spark
Databricks
Deep Learning
TensorFlow
Keras
PyTorch
NATURAL
Science
Modeling
Computer Vision
Econometrics
Job Details
The Data Science & AI (DSAI) organization is a key part of BAM's continued growth. Year over year, the knowledge needed to leverage data plays an increasingly important role in the firm's core business. The analytical expertise that the Data Science & AI organization provides the firm are part of BAM's competitive advantage. The DSAI organization supports Investment Teams across all asset classes by providing research and tools powered by data science and AI to help deliver results and PnL.
As a Geospatial Data Scientist Intern , you will go through a hands-on 12-week program and solve complex, real-world problems. You will experience firsthand how data science and AI are used to enhance and support the investment process. Our internship program includes a two-week training block where you will learn how investing works at a hedge fund and get hands-on experience with our DSAI tools and environments before hitting the desk. It also offers mentorship and collaboration with senior members of the team in addition to the opportunity to expand your network with the greater intern cohort.
In this role, you will be responsible for extracting actionable insights from our many rich alternative datasets with a specific focus on geospatial analytics, including but not limited to imagery analysis, meteorological and hydrological modeling, crop yield forecasting, and spatial data science projects. You will be involved at every stage of this process, including hypothesis generation, data wrangling, building and testing appropriate statistical and machine learning models, and communicating your findings to senior managers.
Responsibilities:
Imagery Processing and Analysis: Acquire, process, and analyze imagery data from both publicly available satellite sources and commercial vendors with demonstrated familiarity with a range of imagery data types (optical, SAR, multispectral, hyperspectral, etc.) and associated metadata.
Generate Insights: Extract actionable investment insights through advanced image processing and modeling techniques such as object detection and change detection to extract actionable insights.
Weather Prediction & Simulation: Utilize information from land, sea, and upper atmosphere to predict or simulate weather conditions and study their causes. Use the latest forecast technology to predict severe weather events such as tornadoes, flooding, hurricanes, strong winds, and extreme temperatures.
Research Design: Develop proof of concepts (POC) in partnership with and with iterative inputs from sponsoring investment teams.
Present Results: Present results to both technical and non-technical audiences
Teamwork & Mindset : Self-driven and results oriented. Perpetually curious while staying exceptionally organized and collaborative in a fast-paced, ambiguous environment.
GIS Platforms: Experience and proficiency with ERDAS, ENVI ESRI, QGIS, or other imagery or GIS platforms. Comfortable working with geospatial data formats.
Qualifications:
PhD student graduating between December 2026 and May 2027 who is pursuing a degree in Earth Sciences, Geospatial Information Systems, Computer Science, Mathematics, Statistics, Data Science 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)
Familiarity with GIS Software (QGIS, ArcGIS)
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: Vector Databases, OpenSearch, Apache Airflow, AWS, Spark, Databricks, Deep learning frameworks (e.g., Tensorflow, Keras and PyTorch)
Academic or industry experience in the following fields is a plus: natural science, spectral imaging, hydrology, meteorology, weather/flood modelling, computer vision, econometrics
Opportunities are available in our New York office.
As a Geospatial Data Scientist Intern , you will go through a hands-on 12-week program and solve complex, real-world problems. You will experience firsthand how data science and AI are used to enhance and support the investment process. Our internship program includes a two-week training block where you will learn how investing works at a hedge fund and get hands-on experience with our DSAI tools and environments before hitting the desk. It also offers mentorship and collaboration with senior members of the team in addition to the opportunity to expand your network with the greater intern cohort.
In this role, you will be responsible for extracting actionable insights from our many rich alternative datasets with a specific focus on geospatial analytics, including but not limited to imagery analysis, meteorological and hydrological modeling, crop yield forecasting, and spatial data science projects. You will be involved at every stage of this process, including hypothesis generation, data wrangling, building and testing appropriate statistical and machine learning models, and communicating your findings to senior managers.
Responsibilities:
Imagery Processing and Analysis: Acquire, process, and analyze imagery data from both publicly available satellite sources and commercial vendors with demonstrated familiarity with a range of imagery data types (optical, SAR, multispectral, hyperspectral, etc.) and associated metadata.
Generate Insights: Extract actionable investment insights through advanced image processing and modeling techniques such as object detection and change detection to extract actionable insights.
Weather Prediction & Simulation: Utilize information from land, sea, and upper atmosphere to predict or simulate weather conditions and study their causes. Use the latest forecast technology to predict severe weather events such as tornadoes, flooding, hurricanes, strong winds, and extreme temperatures.
Research Design: Develop proof of concepts (POC) in partnership with and with iterative inputs from sponsoring investment teams.
Present Results: Present results to both technical and non-technical audiences
Teamwork & Mindset : Self-driven and results oriented. Perpetually curious while staying exceptionally organized and collaborative in a fast-paced, ambiguous environment.
GIS Platforms: Experience and proficiency with ERDAS, ENVI ESRI, QGIS, or other imagery or GIS platforms. Comfortable working with geospatial data formats.
Qualifications:
PhD student graduating between December 2026 and May 2027 who is pursuing a degree in Earth Sciences, Geospatial Information Systems, Computer Science, Mathematics, Statistics, Data Science 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)
Familiarity with GIS Software (QGIS, ArcGIS)
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: Vector Databases, OpenSearch, Apache Airflow, AWS, Spark, Databricks, Deep learning frameworks (e.g., Tensorflow, Keras and PyTorch)
Academic or industry experience in the following fields is a plus: natural science, spectral imaging, hydrology, meteorology, weather/flood modelling, computer vision, econometrics
Opportunities are available in our New York office.
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