AI Data Architect

Looking for the opportunity to work on problems that matter, with colleagues that share your interest and expertise in AI and Machine Learning?  

Leidos is looking for a Senior Data Architect to apply their expertise to help Leidos research, develop, and commercialize technologies enabling data processing at scale for AI consumption. Specific areas of R&D include Data engineering, Data processing, anomaly detection and Data quality management.

We are looking for someone who is intellectually adaptive, likes to collaborate, inquisitive and goal oriented. You will work alongside junior and senior research/data scientists and data engineers with expertise in information retrieval, UI development, information science, machine learning and artificial intelligence, and statistics. You will be leading the teams of engineers and scientists to define and implement research milestones, Proof of Concepts and eventually operationalizing the A-Ready data construction.

Primary Responsibilities.

• Work with and lead a team of data scientists and data engineers on defining and creating scalable AI data architecture strategy
• Identify risks and constraints of AI infrastructure at scale and implement mitigations and workarounds
• Create project plan, milestones and lead the team to Implement the AI data architecture strategy
• Lead the design and implementation of data access controls to produce a data transformation pipeline that discovers, repairs, logs, and deploys quality data plus security policy metadata
• Work with a team to design, build, train, and evaluate machine learning models 
• Be the liaison between executive level leadership, research team and other stakeholders across Leidos.
• Communicate results and methods for solutions to internal and external stakeholders. 
• Work within teams of AI/ML researchers and engineers using Agile development processes 

Basic Qualifications.

• Bachelor's degree in Computer Science, Data Science or related field and over 16 years of relevant experience, Masters with 14 years experience, or PhD with 10 years experience.
• Experience with data architecture at scale and AI Infrastructure
• Experience with data categorization, labeling and tagging
• Experience with data repositories and ETL
• Experience with Data Operations including applying AI for data curation
• Strong Python programming fundamentals 
• Good understanding of machine learning algorithms, tools and platforms
• Experience with AI/ML tools, such as common Python packages (e.g., scikit-learn, NumPy, Pandas)
• Experience with tabular data analysis using languages such as SQL, R, and/or Python 
• Experience with statistical modeling and data analysis
• Experience with leading research projects and project teams
• Experience with planning and execution
• Great communication skills, able to explain model results to a non-technical audience
• Proficient in …