Sr Data Scientist - Applied AI

Kansas City National Security Campus•Kansas City, MO

14h•Hybrid

About The Position

The Sr Data Scientist - Applied AI role leads moderately complex end-to-end design, development, operationalization, and ongoing support of advanced artificial intelligence solutions for manufacturing or business applications, ensuring models are scalable, reliable, and seamlessly integrated into frontend and backend systems.

Requirements

At least five years relevant experience in data science or related technical activities.
BS in engineering from ABET accredited institution or Bachelor of Science degree in data science or related field, OR two additional years of relevant experience.
Ability to travel as determined by the needs of the business.
Ability to work remote, hybrid, or on-site as directed by management and is determined by the needs of the business.
Regular and reliable attendance is an essential function of this job.
United States Citizenship.
Ability to obtain and maintain, if required for position, a U.S. Department of Energy (DOE) security clearance (some positions require additional DOE designations).

Nice To Haves

Experience leading a technical team to deliver business or manufacturing focused solutions.
Ability to grasp complex technical and business problems, prioritize tasks, and devise innovative, production‑ready AI solutions that align with manufacturing or enterprise objectives.
Expert-level proficiency in a modern programming language (e.g., JavaScript, Python, Java, C++) and strong command of database/query languages (e.g., SQL) for building scalable data pipelines and services.
Demonstrated software‑engineering discipline: version control, unit/integration testing, continuous integration‑continuous deployment (CI/CD), and documentation of code and system architecture.
Strong communication skills (verbal, written, presentation) to convey technical concepts to stakeholders, collaborate with cross‑functional teams, and produce clear technical specifications and run‑books.
Deep knowledge of machine‑learning and deep‑learning concepts, including model design, training, evaluation, and optimization for performance, latency, and
Familiarity with distributed data‑processing frameworks and big‑data ecosystems (e.g., Spark, Kafka) to support high‑throughput training and real‑time inference pipelines.
Proven ability to create actionable visualizations or dashboards that show model performance, data quality, and operational metrics for end‑users and decision‑makers.
Hands-on experience deploying AI applications on cloud platforms such as Azure or AWS.
Expert understanding of Large Language Models (LLMs), Natural Language Processing (NLP), and Retrieval-Augmented Generation (RAG).

Responsibilities

Lead the development of AI/ML models and algorithms using disciplined software‑engineering practices, including version control, automated testing, and thorough documentation.
Lead teams to design, build, and maintain production‑grade pipelines for model training, validation, deployment, and monitoring, applying CI/CD and containerization principles.
Architect and expose model inference services via robust APIs, integrating them with stakeholder applications and user interfaces.
Optimize model performance for latency, throughput, and resource utilization to meet real‑time manufacturing or business requirements.
Collaborate with multiple stakeholders to translate business needs into AI system specifications, define service‑level objectives, and provide technical guidance to data‑science partners.
Mentor and support continuous improvement of other engineers, scientists, and interns.