Intermediate Data Scientist

Key Skills

Aartificial intelligence, Apache Hadoop, Azure, Computer Vision (CV), Deep Learning, ETL/ELT technologies, Framework, Keras software library, Machine learning techniques, MxNet, Natural Language Processing (NLP), PyTorch, Snorkel, Summarization, TensorFlow

Job Description

The data science candidate is responsible for collecting, cleaning, and munging data for exploratory data analysis to determine the value of the data and how to properly clean and format the data specific to the machine learning/artificial intelligence algorithms employed. The candidate must be able to analyze the data for the signals and then discriminate between good and bad signals based on the technical project use case. The position requires a powerful 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 of experience with data structures, data/AI modeling, supervised/unsupervised AI algorithms, LLMs, and Generative AI.

Primary Responsibilities

• Create AI prototypes for use cases requiring reinforcement learning, NLP, LLMs, Generative AI, neural nets, and GraphNN.
• Develop and maintain data models (both physical and logical)
• Perform extraction, transform, and load (ETL) tasks related to the different modalities and algorithms applied. This data ETL includes identifying the data’s relevant metadata to ensure consistency, quality, accuracy, integrity, 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 of 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 solutionsSetup and use AWS instances to train and operate AI/ML models

Basic Qualifications

• Master's degree in Computer Science, Mathematics, Statistics, Physics, Computer Engineering
• Must have 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 Text

Preferred Qualifications

• Visualizations/Web Development Skills
• Knowledge of Agentic AI
• 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 the 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 detect

Original Posting Date:

2024-12-19

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:

Pay Range $85,150.00 - $153,925.00

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.