Data Scientist

BRILLIENT CORPORATION•New Carrollton, MD

13d•$120,000 - $140,000•Hybrid

About The Position

The Data Scientist will apply advanced analytical, statistical, and machine learning techniques to analyze complex datasets, develop predictive models, and generate actionable insights within a large data warehouse. The ideal candidate has strong hands-on ETL experience with Python and the broader data science ecosystem, including Jupyter Notebook, Pandas, NumPy, and machine learning frameworks such as scikit-learn. This role requires the ability to work independently with large and complex data sets and tables, translate business and mission requirements into analytical solutions, and communicate technical findings clearly to both technical and non-technical stakeholders.

Requirements

Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Information Technology, or a related technical field.
Minimum five (5) years of professional experience in data science, data analytics, statistical analysis, machine learning, or a related field.
Strong hands-on experience with Python for data analysis, data science, and machine learning.
Experience using Jupyter Notebook for data exploration, analysis, modeling, and documentation.
Strong experience with Pandas and NumPy for data manipulation, analysis, and processing.
Demonstrated experience developing and applying machine learning models, including experience with scikit-learn (sklearn) or comparable machine learning frameworks.
Strong analytical and problem-solving skills, with the ability to work with complex datasets and develop data-driven solutions.
Experience performing data cleaning, transformation, exploratory data analysis, feature engineering, and model evaluation.
Experience working in Linux and Windows operating systems.
Experience developing and using shell scripts for automation and data processing.
Experience working with OpenShift and/or Kubernetes environments.
Ability to communicate complex technical concepts, analytical findings, and recommendations effectively to technical and non-technical audiences.
Ability to work independently and collaboratively in a fast-paced technical environment.

Nice To Haves

Experience with Artificial Intelligence (AI), Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or model fine-tuning.
Experience working with PostgreSQL/Postgres and/or Sybase IQ databases.
Experience with Apache Airflow and automated ETL/data pipelines.
Experience using Git, GitLab, and source control workflows.
Experience using Visual Studio Code (VS Code) or similar integrated development environments.
Experience deploying or operationalizing machine learning models in containerized or cloud environments.
Experience with MLOps practices, model deployment, monitoring, and lifecycle management.

Responsibilities

Perform data preparation, cleaning, transformation, exploration, and feature engineering using Pandas and NumPy.
Analyze large and complex datasets to identify trends, patterns, relationships, and actionable insights.
Develop, implement, evaluate, and maintain statistical and machine learning models to support analytical and mission objectives.
Evaluate model performance, validate results, and identify opportunities to improve model accuracy and reliability.
Collaborate with technical teams, analysts, subject matter experts, and stakeholders to understand requirements and translate them into data-driven solutions.
Use Python as the primary programming language for data analysis, modeling, automation, and data science applications.
Develop and document analytical workflows and models using Jupyter Notebook.
Develop and execute scripts to support data processing, automation, and analytical workflows.
Apply machine learning techniques and frameworks, including scikit-learn, to develop and evaluate predictive and analytical models.
Conduct exploratory data analysis and statistical analysis to support data-driven decision-making.
Work with structured and unstructured data from multiple sources and formats.
Develop repeatable and scalable analytical processes and workflows.
Document methodologies, analytical approaches, models, assumptions, results, and recommendations.
Present complex analytical findings and technical results in a clear and understandable manner.
Work in both Linux and Windows operating environments.
Work with containerized and/or distributed environments using OpenShift and Kubernetes.
Maintain awareness of emerging data science, machine learning, and analytical technologies and identify opportunities to apply them to mission requirements.