Job description

Vantor is forging the new frontier of spatial intelligence, helping decision makers and operators navigate what’s happening now and shape what’s coming next. Vantor is a place for problem solvers, changemakers, and go-getters—where people are working together to help our customers see the world differently, and in doing so, be seen differently. Come be part of a mission, not just a job, where you can: Shape your own future, build the next big thing, and change the world. To be eligible for this position, you must be a U.S. Citizen. This position requires an active U.S. Government security clearance, applicants who do not currently hold the required clearance will not be eligible for consideration. Employment for cleared roles is contingent upon verification of clearance status. Export Control/ITAR: Certain roles may be subject to U.S. export control laws, requiring U.S. person status as defined by 8 U.S.C. 1324b(a)(3). Please review the job details below. This position requires an active U.S. Government Security Clearance at the TS/SCI level with CI polygraph. 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. Responsibilities: This position is fulltime on- site in Reston, VA. 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. Minimum Qualifications: 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. Preferred Qualifications: 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 . Pay Transparency: To support pay transparency, Vantor includes salary ranges in all U.S. job postings. Starting pay for this role will fall within the listed range and will be based on factors such as experience, qualifications, skills, location, and market conditions. Candidates who meet the minimum requirements for the role should not expect to receive compensation at the top of the range. The listed range reflects the expected pay for this position, and final offers will be determined based on each candidate’s experience, expertise, and alignment with the role. ● The base pay for this position within the Washington, DC metropolitan area is: $140,000.00 - $187,000.00 - $205,700.00 annually. For all other states, we use geographic cost of labor as an input to develop market-driven ranges for our roles, and as such, each location where we hire may have a different range. Benefits: Vantor offers a competitive total rewards package that goes beyond the standard, including a robust 401(k) with company match, mental health resources, and unique perks like student loan repayment assistance, adoption reimbursement and pet insurance to support all aspects of your life. You can find more information on our benefits at: vantor.com The application window is three days from the date the job is posted and will remain posted until a qualified candidate has been identified for hire. If the job is reposted regardless of reason, it will remain posted three days from the date the job is reposted and will remain reposted until a qualified candidate has been identified for hire. The date of posting can be found on Vantor s Career page at the top of each job posting. To apply, submit your application via Vantor s Career page. EEO Policy: Vantor is an equal opportunity employer committed to an inclusive workplace. We believe in fostering an environment where all team members feel respected, valued, and encouraged to share their ideas. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender identity, sexual orientation, disability, protected veteran status, age, or any other characteristic protected by law.

Requirements

Must-haves

Please review the job details below.
This position requires an active U.S. Government Security Clearance at the TS/SCI level with CI polygraph.
This position is fulltime on- site in Reston, VA.
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.

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 .

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

gomachine learningaillmpythonsqlawsazuregcpmldockerkubernetes

Platform: workday
Effort: long

Benefits

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Vision

401(K)