Data Scientist

QTC Management, Inc.

Senior Data Scientist leading advanced analytics and AI capabilities at Leidos QTC Health Services. Focusing on machine learning model development and deployment for impactful health services solutions.

Posted 5/22/2026full-timeRemote • Florida, Texas, Virginia • 🇺🇸 United StatesSeniorLeadWebsite

Tech Stack

Tools & technologies

AWSCloudFlaskNoSQLPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Design, develop, and deploy machine learning models, including LLMs, transformer-based models, and traditional ML approaches
Build and optimize NLP, recommendation systems, and predictive analytics solutions to address complex business problems
Develop and implement RAG (Retrieval-Augmented Generation), prompt engineering strategies, and emerging agentic AI workflows
Conduct rigorous model evaluation using appropriate statistical methods and performance metrics
Acquire, integrate, and preprocess structured and unstructured data from diverse sources (e.g., relational databases, NoSQL systems, logs, external datasets)
Perform feature engineering and data preparation to ensure high-quality inputs for ML models
Ensure data quality, validation, and governance compliance for AI/ML use cases
Design and maintain scalable data and ML pipelines to support model training, evaluation, deployment, and monitoring
Deploy models into production using cloud-native architectures and APIs
Implement model monitoring, drift detection, and retraining strategies to ensure sustained performance
Contribute to CI/CD pipelines and ML lifecycle automation
Contribute to system architecture decisions across data, ML, and cloud platforms
Provide technical leadership in selecting tools, frameworks, and design patterns for AI/ML solutions
Mentor junior team members and promote best practices in data science and ML engineering
Partner with stakeholders, product owners, and subject matter experts to translate business needs into AI/ML solutions
Communicate findings, insights, and recommendations clearly to both technical and non-technical audiences
Deliver solutions that drive measurable improvements in efficiency, quality, and business outcomes.

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field
9+ years of relevant professional experience
Strong experience in data science, machine learning, or applied AI roles
Strong proficiency in Python (required); experience with R is a plus
Hands-on experience with ML frameworks such as PyTorch, TensorFlow, and scikit-learn
Proven experience developing and deploying LLMs, NLP models, or deep learning systems
Experience with LLM ecosystems (e.g., Hugging Face, LangChain, vector databases, RAG architectures)
Strong experience with SQL and data manipulation across relational and non-relational databases
Experience building and deploying ML solutions in cloud environments (AWS preferred: SageMaker, Bedrock, Lambda, S3, etc.)
Experience with API development (e.g., FastAPI, Flask) and integrating ML models into production systems
Solid understanding of statistics, model evaluation, and experimental design
Demonstrated ability to work across the full ML lifecycle (data → modeling → deployment → monitoring)
Strong problem-solving skills
Excellent communication skills with the ability to influence technical and business stakeholders.

Benefits

Comp & perks

Competitive compensation
Health and Wellness programs
Income Protection
Paid Leave
Retirement

ATS Keywords

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Hard Skills & Tools

machine learningnatural language processingpredictive analyticsfeature engineeringdata preparationmodel evaluationmodel monitoringcloud-native architecturesAPI developmentCI/CD pipelines

Soft Skills

technical leadershipmentoringcommunicationproblem-solvingstakeholder engagement