Data Scientist

Are you passionate about solving complex problems using AI and Machine Learning? Are you excited to build cutting-edge technology and contribute to innovative research that drives real-world impact?

Leidos is looking for a Data Scientist to join our team and contribute to Research and Development efforts spanning areas such as (but not limited to) Natural Language Processing, Reinforcement Learning, Agentic AI and Predictive Analytics This is an excellent opportunity to collaborate with a multidisciplinary team of experts and make meaningful contributions to the field of AI/ML.

 We are looking for someone who is intellectually adaptive, likes to collaborate, inquisitive, and capable of conducting original science.  You will work closely with junior and senior research/data scientists with expertise in information retrieval, large language models, information science, artificial intelligence, and statistics. You should be strongly experienced in Python coding and research methodologies and have knowledge of commonly used ML libraries (SciKit-learn). 

Primary Responsibilities

• Design build, train, and evaluate machine learning models across various domains.
• Conduct original research and develop innovative solutions in areas such as Predictive Analytics, Explainable AI, and statistical modeling.
• Collaborate with AI/ML researchers and engineers using Agile methodologies.
• Analyze complex datasets to identify trends and inform decision-making processes.
• Communicate findings, insights, and methodologies to both technical and non-technical stakeholders.
• Contribute to the development of tools and technologies for deployment in real-world environments.
• Implement appropriate modeling and data science strategies required to address customer needs

Basic Qualifications

• Bachelor's degree in Computer Science, Data Science or related field and 4+ years of relevant experience, or a Master’s degree with at least 2 years of relevant experience.
• Strong Python programming fundamentals.
• Experience with AI/ML tools, such as common Python packages (e.g., scikit-learn, NumPy, Pandas) and Jupyter notebooks.
• Knowledge of machine learning algorithms, statistical modeling, and data analysis techniques.
• Familiarity with research methodologies and practices, including experimental design and hypothesis testing.
• Familiarity with deployment into virtualized and containerized environments (e.g., VMware, Docker, Kubernetes)
• Self-starter with high intellectual curiosity.
• Strong communication skills, with the ability to explain complex technical concepts to a non-technical audience.
• Ability to obtain a Secret clearance

Preferred Qualifications

• Practical understanding of Large Language Models (LLMs) and agent frameworks such as LangChain, LangGraph, CrewAI,A2A, MCP, or AutoGen
• Familiarity with data visualization libraries such as Plotly, Streamlit, and matplotlib. 
• Solid understanding and hands-on experience with generative AI models including prompt engineering, chain-of-thought reasoning, and Natural Language Processing (NLP) tasks such as entity extraction, summarization, and semantic search.
•  Experience with the Software Development Lifecycle (SDLC), including DevSecOps practices
• Hands-on experience with AI service integration such as NIMS, Azure OpenAI, Bedrock, GCP Vertex AI
• Proficiency in scripting with Linux Bash, PowerShell, or equivalent automation tools

LeidosAI

At Leidos, we don’t want someone who "fits the mold"—we want someone who melts it down and builds something better. This is a role for the restless, the over-caffeinated, the ones who ask, “what’s next?” before the dust settles on “what’s now.”

If you’re already scheming step 20 while everyone else is still debating step 2… good. You’ll fit right in.

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.