Data Scientist II - Applied AI

Kansas City National Security Campus•Kansas City, MO

15h•Hybrid

About The Position

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

Requirements

BS in engineering from ABET accredited institution or Bachelor of Science degree in data science or related field, or two years of relevant experience in lieu of a degree.
Two or more years of relevant experience in data science or related technical activities.
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 a U.S. Department of Energy (DOE) security clearance (some positions require additional DOE designations)

Nice To Haves

Ability to grasp complex technical and business problems, prioritize tasks, and devise innovative, production‑ready AI solutions that align with manufacturing or enterprise objectives.
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 resource utilization.
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.
Experience with API development, microservices architecture, and version control systems (Git).
Hands-on experience deploying AI applications on cloud platforms such as Azure or AWS.
Solid understanding of Large Language Models (LLMs), Natural Language Processing (NLP), and Retrieval-Augmented Generation (RAG).

Responsibilities

Lead and support moderate to highly complex data science and analytics projects in support of process improvement, defect reduction, and predictive analytics for manufacturing and business applications.
Independently develop solutions using disciplined software development processes, making recommendations for developing new code or re-using existing code, implementing version control, and maintaining documentation of created applications.
Develop AI/ML models and algorithms using disciplined software‑engineering practices, including version control, automated testing, and thorough documentation.
Support design, build, and maintenance of production‑grade pipelines for model training, validation, deployment, and monitoring, applying CI/CD and containerization principles.
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.
Contribute to the production of information products, supporting visualization and data accessibility in a customer centric manner.
Evaluate and make recommendations regarding technical advances that improve productivity and quality, reduce flow times, and enhance operational surety.
Develop and implement machine learning models to inform business needs and decisions.
Perform data processing and transformation to targeted audience.