Data Scientist Stf

QTC Management, Inc.•, TX

8d•$112,608 - $152,352

About The Position

Leidos QTC Health Services (LQTC) is seeking a Senior Data Scientist to join a fast-paced, innovative team focused on delivering scalable AI/ML solutions across the enterprise. This role is ideal for a highly experienced practitioner who combines strong statistical and machine learning expertise with the ability to design and deploy production-grade systems. You will lead the development of advanced analytics and AI capabilities, including large language models (LLMs), NLP, and predictive modeling, while working across the full lifecycle from data exploration and prototyping to deployment and monitoring in cloud environments. This position requires a balance of hands-on modeling, ML system design, and cross-functional collaboration, enabling you to drive measurable business impact across multiple lines of business.

Requirements

Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field. Equivalent combination of education and relevant experience will be considered
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 and ability to operate in a fast-paced, evolving environment
Excellent communication skills with the ability to influence technical and business stakeholders

Nice To Haves

Experience with healthcare or medical data (e.g., EHR, claims data, clinical NLP, FHIR standards)
Hands-on experience with agentic AI systems and multi-agent orchestration frameworks
Strong background in MLOps and production ML systems, including CI/CD and model lifecycle management
Experience with distributed data processing frameworks (e.g., Spark, PySpark, Ray)
Familiarity with vector search technologies (e.g., FAISS, OpenSearch, Pinecone)
Experience working in regulated environments with data governance and compliance requirements

Responsibilities

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