Camden, Arkansas; Chelmsford, Massachusetts; Grand Prairie, Texas; Orlando, Florida
Type
Full Time
Responsibilities:
Day-to-day tasking and management of the MFC Data and Operations Technology team. Including managerial tasks such as travel approval, time approval, performance reviews, etc.
Identifying new business opportunities for the team.
Managing relationships with stakeholders and potential stakeholders.
Understanding business needs and translating them into data problems.
Helping to architect solutions to data problems in the form of data products.
Tracking projects and driving work on them through to completion.
Building business processes for more efficient operation of the team.
Implementing best technical practices from the fields of machine learning and software engineering.
Identifying tooling gaps on the team and working to fill those gaps.
Hiring and mentoring team members.
Helping to plan the strategic vision of the team.
Spreading best practices of the team to other data science teams at Lockheed Martin by creating knowledge materials, presenting those materials, and engaging with other data science leaders on those materials.
Requirements & Skills:
10+ years of professional experience applying statistics, predictive modeling, and machine learning techniques to solve business problems
Experience leading analytics projects from problem definition and data discovery to data preparation, feature engineering, model selection, validation, and deployment
Excellent verbal/written communication skills
Ability to effectively present findings and make recommendations to technical and non-technical business stakeholders in a clear and compelling manner
Demonstrated creativity and innovation in identifying valuable ways of incorporating machine learning into business workflows and processes
Functional or technical leadership experience
Project management experience
Superlative organizational and interpersonal skills
Experience with one or more programming languages (e.g. Python, R, Java, MATLAB, etc.)
The ability to stay updated on new technologies in the rapidly evolving field of AI
Experience with the design, implementation, and evaluation of models, agents, and software prototypes for real-world AI
Bridge the gap between fundamental research and products by addressing research questions arising from real-world problems and integrating novel research into applications.
Hands-on experience building with generative AI models and developing full-stack applications leveraging LLMs and/or multi-modal models
Ability to design and conduct experiments to evaluate AI models
Knowledge of data engineering best practices and cloud computing technologies (preferably AWS or Azure).
Experience with or expertise in manufacturing and/or engineering processes.
Knowledge of industry best practices for developing and deploying AI systems.
