Data Scientist/Machine Learning Engineer

Responsibilities

The responsibilities include, but are not limited to:

• Research, design, implement, and deploy Machine Learning algorithms for enterprise applications.
• Assist and enable federal customers to build their own applications.
• Contribute to the design and implementation of new features.
• Hands-on experience deploying and operating applications using IaaS and PaaS on major cloud providers, such as Amazon AWS, Microsoft Azure, or Google Cloud Services.
• Experience with deep learning, natural language processing, computer vision, or reinforcement learning.
• Real passion for developing team-oriented solutions to complex engineering problems.
• Thrive in an autonomous, empowering and exciting environment.
• Great verbal and written communication skills to collaborate multi-functionally and improve scalability.
• Interest in committing to a fun, friendly, expansive, and intellectually stimulating environment.
• Convey highly technical concepts and information in written form to technical and non-technical audiences.
• The ability to work on multiple concurrent projects is essential.
• Strong self -motivation and the ability to work with minimal supervision.
• Must be a team-oriented individual, energetic, result & delivery oriented, with a keen interest on quality and the ability to meet deadlines.
• Ability to work in an agile environment.
• Own machine learning models end-to-end, from data exploration and experimentation through production deployment, monitoring, and iteration.
• Design, train, and evaluate machine learning models using TensorFlow, Keras, and scikit-learn (or equivalent Python ML frameworks) to predict healthcare events and identify patients needing specific treatments, screenings, and interventions across multiple medical specialties.
• Work with large-scale healthcare and consumer datasets (millions+ of records)
• Build and maintain ETL workflows and data pipelines in BigQuery and Python to ensure data consistency, quality, and usability across complex healthcare datasets including EHR data, medical coding systems, and patient demographics.
• Continuously evaluate, tune, and improve models in production through hyperparameter optimization, performance monitoring, and error analysis to maximize accuracy, scalability, and real-world impact.
• Implement patient matching algorithms using deterministic and probabilistic techniques to link patient records across disparate healthcare systems.
• Partner closely with engineering, QA, and product teams to align models and infrastructure with business goals while maintaining high standards for production code quality and system reliability.
• Clearly communicate technical concepts and model results to both technical and non-technical stakeholders to drive data-informed decisions.

Requirements

• Bachelor's degree in Computer Science, Electrical Engineering, Statistics, or equivalent fields required.
• Minimum 2 years relevant work experience preferred.
• Excellent programming skills in Python.
• Applied Machine Learning experience (regression and classification, supervised, and unsupervised learning).
• Strong mathematical background (linear algebra, calculus, probability, and statistics).
• Ability to drive a project and work both independently and in a team.
• Smart, motivated, can-do attitude, and seeks to make a difference.
• Excellent verbal and written communication.