Senior Data Scientist

Vantor•Reston, VA

18d•Onsite

About The Position

Vantor is seeking a Senior Data Scientist who thrives on working with big data and delivering data-centric solutions that drive customer impact. The ideal candidate is a proactive problem-solver who can independently identify analytical needs, develop scalable solutions, and clearly communicate insights to stakeholders. This role requires both technical depth and the ability to operate autonomously in a fast-paced, mission-driven environment. This project supports a hard and deeply buried target mission set, providing critical analytic insights to government stakeholders. Success depends on the development of accurate, scalable, and mission-tailored data solutions that enable timely and informed decision-making. The work directly impacts national security objectives by transforming complex data into actionable intelligence in a highly specialized and sensitive domain.

Requirements

Current/active TS/SCI security clearance and be willing and able to obtain CI polygraph.
10 years of experience in data science, analytics, or a related technical field.
Master ’s degree in data science, computer science, engineering, statistics, GIS, or related discipline. Degree can be substituted with an additional 2 years of experience.
Proficient in Python, SQL, and tools/libraries for data analysis, machine learning, and automation.
Demonstrated ability to independently conduct full-cycle data projects, from processing to reporting.
Strong verbal and written communication skills; able to explain complex findings clearly.
Familiarity with foundational LLM/NLP concepts and experience applying pre-trained models (e.g., GPT) for internal automation or data summarization.
Ability to work with minimal supervision and produce logically structured, actionable insights.
Ability to c ollaborate with business stakeholders to define analytical requirements and deliver data-driven solutions.
Demonstrated experience deploying graph databases or graph-based applications into production environments.

Nice To Haves

Expertise in advanced statistical and machine learning techniques, including NLP and unsupervised learning.
Experience developing or fine-tuning LLMs and integrating them into knowledge systems or workflows.
Strong background with cloud-based platforms (e.g., AWS, Azure, GCP) and scalable data processing tools.
Track record of designing or enhancing knowledge management solutions using AI or LLMs.
Experience building internal tools or bots for search, summarization, or decision support using LLMs.
Awareness of responsible AI practices and ethical considerations in LLM deployment.
Knowledge of MLOps practices and tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML).
Experience with big data technologies such as Spark, Databricks, or Hadoop.
Familiarity with generative AI, large language models (LLMs), retrieval-augmented generation (RAG), or AI agent frameworks.
Experience with data visualization tools such as Power BI, Tableau, or Looker.
Knowledge of containerization and orchestration technologies such as Docker and Kubernetes.
Strong understanding of graph data modeling, graph algorithms, and graph query languages such as Cypher, Gremlin, or SPARQL.

Responsibilities

Automate and optimize data extraction, cleaning, processing, and analysis tasks using scripting languages and ETL pipelines.
Process and analyze large-scale, structured and unstructured datasets using big data frameworks (e.g., Spark, Hadoop, or cloud-native equivalents).
Independently conduct end-to-end data science/engineering workflows with minimal supervision, from hypothesis generation to delivery of actionable insights.
Explore opportunities to combine graph technologies, machine learning, generative AI, and large language models to create new analytical capabilities.
Clearly document methodologies and findings in formal written reports and present outcomes to both technical and non-technical audiences.
Design, fine-tune, and implement Large Language Models (LLMs) into workflows to support knowledge management systems, including: Development of LLM-driven tools to automate summarization, tagging, and retrieval of internal data, reports, and documentation. Integration of LLMs into workflows to streamline knowledge capture, institutional memory, and information sharing. Deployment of LLM-based internal assistants to support decision-making and reduce redundant effort.
Translate ambiguous business or customer problems into well-defined analytical approaches, experiments, and measurable outcomes.
Present findings and recommendations through clear visualizations, reports, and presentations.
Mentor junior data scientists and contribute to best practices in data science and analytics.
Partner with engineering teams to operationalize machine learning models in production.