This position requires a very strong general knowledge of machine learning and artificial intelligence so that the candidate can contribute to various machine learning (ML) and artificial intelligence (AI) projects in the Leidos AI/ML Accelerator. Candidates will have two years or less experience finding and aligning signals across multiple modalities (image, video, audio, lidar, etc..). Along with those skills, the candidate must have demonstrated ability working independently and in technical teams to implement and customize algorithms to fusion multiple data modalities. In this data scientist position at Leidos Arlington, VA. office, the candidate we be a strong Python coder with knowledge in using ML libraries like SciKit Learn, TensorFlow, Keras on both CPU and GPU compute architectures.

Primary Responsibilities

• Create AI/ML prototypes for use cases requiring identifying entity/objects, determining object association, object disambiguation, anomaly detection, state estimations, etc.
• Develop and maintain data models (both physical and logical)
• Perform extraction, transform, and load (ETL) tasks related to the different modalities and algorithms being applied. This data ETL includes identifying  the data’s relevant metadata to ensure consistency, quality, accuracy, integrity, and information assurance and security.
• Conduct anomaly detection using various AI/ML techniques
• Use algorithms to identify complex patterns across multiple modalities
• Increase the efficiency and quality data alignment and fusion
• Enhance and maintain analysis tools, including automation of current processes using AI/ML algorithms
• Direct quantitative data analysis including developing retrieval, processing, fusion, analysis, and visualization of various datasets
• Configure and program prototypes Jupyter notebooks with ML solutions
• Setup and use AWS instances to train and operate AI/ML models

Basic Qualifications

• 8+ years of relevant experience and a B.S. degree in Aerospace Engineering, Computer Science, Mathematics, Statistics, Physics, Electrical Engineering, Computer Engineering; or a Master's and 6+ years of relevant experience
• Must be able to obtain a Top Secret security clearance with a polygraph security clearance
• Experience with Deep Learning Frameworks such as Keras, Tensorflow, PyTorch, Mxnet, etc. - Ability to apply these frameworks to real problems in the ‘time -series’ domain
• Practical hands-on experience and the ability to explain statistical analysis, reinforcement learning, transfer learning, natural language processing, and computer vision
• Expert software development skills lifecycle including developing and maintaining good production quality code
• Hands-on Software Development Skills (Python-Preferred)
• Experience or educational courses/projects in Machine Learning, and/or Text

Preferred Qualifications

• Visualizations/Web Development Skills (e.g. Tableau, D3, etc).
• Hands-on experience with prototype development
• Hands-on experience with automating data cleansing, formatting, staging, and transforming data human
• Hands-on experience applying data analytics
• Hands-on experience with intelligent systems and machine learning
• Experience with interpretability of deep learning models
• Big Data Skills (Azure, Hadoop, Spark, recent deep learning platforms)
• Experience with text mining tools and techniques including in areas of summarization, search (e.g. ELK Stack), entity  extraction, training set generation (e.g. Snorkel) and anomaly detection

Original Posting Date:

While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

Pay Range:

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.