Role Overview

We are seeking a Mid-Level Data Scientist to support applied analytics and machine learning efforts across large, complex data environments. This is a hands-on, execution-focused role working closely with Data Engineering, MLOps, and Platform teams to develop models and insights that operate reliably in production systems.

The ideal candidate is comfortable working with complex data pipelines and data lakes, understands data quality challenges, and is eager to grow within regulated and security-conscious AI environments.

Key Responsibilities

Data Science & Analytics

• Develop, test, and evaluate statistical and machine learning models using structured and unstructured data

• Perform exploratory data analysis on high-volume, complex datasets to identify trends and predictive signals

• Support feature engineering, data preprocessing, and model validation efforts

• Apply statistical methods to evaluate model performance, reliability, and accuracy

Data Pipelines & Platforms

• Partner with Data Engineering teams to integrate models into enterprise data pipelines and data lake architectures

• Analyze data from databases, object storage, APIs, and other heterogeneous sources

• Ensure data quality, consistency, and traceability across ingestion and transformation workflows

• Support analytics and machine learning workflows operating at scale

Collaboration & Delivery

• Collaborate with ML Engineers, MLOps, and DevOps teams to support production ML workflows

• Communicate analytical findings through documentation, visualizations, and briefings

• Contribute to model documentation, validation artifacts, and continuous improvement initiatives

Required Qualifications

• U.S. Citizen with an active DoD, Intelligence Community, or DHS clearance, or eligibility to obtain and maintain one

• Bachelors degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field, or equivalent professional experience

• 5 to 7 years of professional experience in data science, applied analytics, or machine learning roles

• Proficiency in Python and common data science libraries

• Experience working with large, complex datasets in enterprise or data lake environments

• Strong understanding of statistical methods and core machine learning techniques

Preferred Qualifications

• Experience supporting analytics or ML solutions in medical, biotech, bioscience, or healthcare environments

• Familiarity with HIPAA-regulated data, including PHI handling and de-identification practices

• Experience operating in regulated or security-conscious environments

• Familiarity with cloud-based data platforms, Azure preferred

• Exposure to distributed data processing frameworks such as Spark

• Experience collaborating with MLOps or production ML teams

Benefits and Growth

• Competitive salary and comprehensive health benefits

• 401(k) with company matching

• Clearance sponsorship for eligible candidates

• Training and certification support

• Opportunity for growth into Senior Data Scientist roles