Sr. Data Scientist- Computer Vision, Data & Machine Learning (DML)

Amazon
Northern Virginia / local Amazon office2026-02-13ONSITE

About the job

Join a sizeable team of data scientists, research scientists, and machine learning engineers that develop computer vision models on overhead imagery for a high-impact government customer. We own the entire machine learning development life cycle, developing models on customer data.

Responsibilities

Run data conversion pipelines to transform customer data into the structure needed by models for training

Perform EDA on the customer data

Train deep neural network models on overhead imagery

Develop and implement hyper-parameter optimization strategies

Test and Evaluate models and analyze results

Package and deliver models to the customer

Incorporate model R&D from low-side researchers

Implement new features to the model development code base

Collaborate with the rest of the team on long term strategy and short-medium term implementation.

Contribute to presentations to the customer regarding the team’s work.

Qualifications

Minimum

5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience

Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue)

Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science

2+ years of experience with machine learning, statistical modeling, data analysis tools and techniques, and performance parameters for computer vision delivery requirements

Current, active US Government Security Clearance of Top Secret or above

Preferred

3+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience

Experience managing data pipelines

Experience as a leader and mentor on a data science team

Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science

Familiarity with internals of LLMs, VLMs, and traditional Computer Vision object detection models