Data Scientist 4

Gormat•Annapolis Junction, MD

About The Position

We are seeking a Data Scientist who is an established AI expert with demonstrable experience in designing and implementing sophisticated AI/ML and data science solutions across various domains within the customer environment. You will help to establish an authoritative role in a customer-prioritized and highly visible AI system that will be of critical use. You will need to to have experience designing and building customized capabilities to enable Retrieval Augmented Generation (RAG) for multiple enterprise data sets, agentic AI systems to automate and orchestrate decision-making actions across specialized AI resources, and Docker-based microservices, user-facing GUIs, and cloud-hosted services (Kubernetes architecture) hosted on AWS/Azure/Google service platforms. attribution. The Level 4 Data Scientist shall possess the following capabilities: Foundations: (Mathematical, Computational, Statistical). Data Processing: (Data management and curation, data description and visualization, workflow and reproducibility). Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations). Ability to make and communicate principal conclusions from data using elements of mathematics, statistics, computer science, and applications-specific knowledge. Ability to use analytic modeling, statistical analysis, programming, and/or another appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique feature and limitations inherent in Government data holdings. Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist others with drawing appropriate conclusions from the analysis of such data. Effectively communicate complex technical information to non-technical audiences. Need an established AI expert with demonstrable experience designing and implementing sophisticated AI/ML and data science solutions across various domains within the MPO customer environment. Candidate must be familiar with design and implementation of Agentic AI systems and customized Retrieval Augmented Generation (RAG) capabilities. Candidate must possess deep knowledge and experience in AI solutions and an ability to assess, evaluate, and implement the correct ML models for specific purposes and optimized performance. They must have experience generating, evaluating, training and optimizing Machine Learning models (i.e. LLMs) for use in low-memory, low-resourced edge devise environments. Expert proficiency in python is required.

Requirements

Bachelor's Degree with 15 years of relevant experience.
Associate's degree with 17 years of experience may be considered for individuals with in-depth experience that is clearly related to the position.
Bachelor's Degree must be in Mathematics, Applied Mathematics Statistics, Applied Statistics, Machine learning, Data Science, Operations Research, or Computer Science or a degree in a related field (Computer Information Systems, Engineering), a degree in the physical/hard sciences (e.g. physics, chemistry, biology, astronomy), or other science disciplines with a substantial computational component (i.e. behavioral, social, or life) may be considered if it included a concentration of coursework (5 or more courses) in advanced Mathematics (typically 300 level or higher, such as linear algebra, probability and statistics, machine learning) and/or computer science (e.g. algorithms, programming, , data structures, data mining, artificial intelligence).
Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least on high level language (e.g. Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering.
Experience with designing and building: Customized capabilities to enable Retrieval Augmented Generation (RAG) for multiple enterprise data sets. Agentic AI systems to automate and orchestrate decision-making actions across specialized AI resources. Docker-based microservices, user facing GUIs, and Cloud-hosted services (Kubernetes architecture) hosted on AWS/Azure/Google service platforms.
Deep knowledge and experience in AI solutions and an ability to assess, evaluate, and implement the correct ML models for specific purposes and optimized performance.
Experience generating, evaluating, training, and optimizing Machine Learning models (i.e. LLMs) for use in low-memory, low-resourced edge device environments.
Proficient in the development and application of advanced analytical algorithms, advanced statistical techniques, statistical outlier detection, data clustering, and graph-based analysis.
Capable of using data processing and data automation techniques for ETL operations and injection into ELK systems and databases.
Familiar with graph theory, graph algorithms (i.e. community detection), techniques to design and generate large-scale graphs (nodes and links) from diverse data sources, and techniques to isolate highly relevant graph entities.
Familiar with domain-specific data and knowledge related to cybersecurity tradecraft and knowledge resources (enterprise data sources, data extraction tools, data analysis/interpretation techniques, data policy and compliance, and data curation considerations).
Self-sufficient, self-starter comfortable with working on a collaborative team of highly skilled data scientists and system engineers.
Able to collaborate with technical mission stakeholders and colleagues for strategic and technical planning.
Established technical experience and expertise with: Python for Data Science (PySpark, Pandas, etc.) and Machine Learning (spaCy, PyTorch, etc.). Natural Language Processing (NLP) for text splitting, segmentation, classifiers, labeling, tokenization, and semantic ontology. Graph tools such as Neo4j, RedisGraph, or GraphFrames. ELK Stack (Elastic Stack): Elasticsearch, Logstash, Kibana. ETL to PostgreSQL, MongoDB, Neo4j, Redis. Kubernetes, Docker, Kafka. Hugging Face. AWS, Azure. VM (Linux, Ubuntu) configuration for individual and team profiles. DevX/Coder.
TS/SCI with polygraph is required.