Data Scientist
Role details
Job location
Tech stack
Job description
- As an individual contributor, requires a moderate level of instruction, guidance, and direction from more senior staff and is progressing toward working independently.
- Provides informal guidance to new or junior team members.
- Identifies and solves routine problems; Analyzes possible solutions using technical experience and established precedents within the Data Science and Data Engineering disciplines.
- Leverages intermediate level of knowledge of Data Science and Data Engineering and applies it to individual and team work products.
- Communicates complex information, both written and verbal, to technical and non-technical audiences in routine situations.
Requirements
The successful candidate for this position is a hybrid Data Scientist and Data Engineer who combines strong foundations in computer science, mathematics, and software engineering with hands-on experience designing data systems and applying advanced analytics, machine learning, and AI techniques to solve mission problems. This role requires practitioners who can operate across the full data lifecycle from data modeling, processing, and infrastructure engineering to statistical analysis, machine learning, experimentation, and operational deployment. Our organization values innovation and believes that keeping up with the latest research and technologies is essential.
The candidate should have hands-on experience in several of the areas specified below, however it is not required that a candidate has experience in all of these areas.
Advanced Data Analytics and Artificial Intelligence
- Machine Learning and Model Evaluation (e.g., supervised, unsupervised, deep learning, reinforcement learning)
- Statistical Analysis and Exploratory Data Analysis (e.g., descriptive statistics, regression, visualization)
- Large Language Models and AI-Assisted Workflows
- Natural Language Processing (e.g., summarization, classification, entity recognition, topic modeling)
- Anomaly Detection and Predictive Modeling
- Graph Analytics (e.g. centrality, similarity, link prediction)
Data Engineering
- Distributed Data Processing and Big Data Architectures (e.g., Spark, Hadoop, Ray, Dask, Kafka)
- Data Pipeline/ETL Development and Orchestration (e.g., Airflow, Dagster, NiFi)
- Batch and Streaming Data Processing
- Data Modeling, Schema Design, and Data Quality Assessment
- Data Governance, Access Control, and Secure Data Handling
- Cloud-based Data Platforms and Storage Architectures
Software Development and MLOps
- Computer Programming and Databases - Proficiency in Python and SQL required
- Software Engineering Best Practices (e.g., version control, testing, modular design)
- MLOps and Model Operationalization (e.g., model deployment, monitoring, reproducibility, containerization)
- Linux-based Development Environments, Git, Containers, and Cloud Platforms, * Bachelor's degree in Computer Science, Applied Mathematics, Statistics, Data Science, or related field with 2 years of relevant experience.
- Hands-on proficiency with advanced Data Science techniques, including Machine Learning (e.g., supervised, unsupervised, deep learning, reinforcement learning), anomaly detection, statistical analysis, and/or analysis of very large datasets.
- Computer programming skills (e.g., Python, R, C/C++, Java, Scala, Julia, Rust, MATLAB).
- The ability to obtain and maintain a DoD Top Secret clearance within 12 months of hire.
- Per the U.S. Government's eligibility requirements, you must be a U.S Citizen to be considered for a security clearance.
- This position requires a minimum of 50% hybrid on-site, * Master's degree in Computer Science, Applied Mathematics, Statistics, Data Science, or related field
- Active DoD Top Secret Clearance.
This requisition requires the candidate to have a minimum of the following clearance(s)