Mid-Level Data Scientist

Vantor•Reston, VA

18h•Onsite

About The Position

Vantor is seeking a Mid-Level Data Scientist to play a key support role within the data science team, responsible for automating data workflows, cleaning and preparing large-scale datasets, and ensuring that senior data scientists have reliable, well-structured data for advanced analytics and modeling. This position requires strong technical skills in data wrangling, scripting, and big data processing frameworks. The ideal candidate is comfortable working in modern cloud environments, has familiarity with containerization, and is capable of handling data pipelines independently, with some exposure to tools like Clairvoyant for orchestration or pipeline development. 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.
5 years of professional experience in data science, analytics, or data engineering roles.
Bachelor’s degree in data science, computer science, engineering, statistics, GIS, or related discipline. Degree may be substituted with an additional 2 yrs of experience.
Proficiency in Python and SQL; familiarity with R is a plus.
Experience with data wrangling libraries (e.g., pandas, PySpark) and working with APIs or batch processing tools.
Experience working with SQL and relational databases.
Understanding of data modeling, metadata management, and data governance principles.
Proficient with Tableau or similar dashboard tools.
Exposure to cloud platforms such as AWS, Azure, or GCP, especially in data-related services.
Strong attention to detail and documentation practices.

Nice To Haves

Master’s degree in a technical field (e.g., Data Science, Computer Science, Engineering).
Experience using Clairvoyant or similar orchestration platforms to manage and monitor data pipelines.
Familiarity with container orchestration tools like Docker, Rancher, or Kubernetes.
Basic knowledge of LLM and NLP concepts, including using pre-trained models for simple tasks like summarization or keyword extraction.
Basic familiarity with GIS tools and data
Experience with graph data science algorithms and graph analytics.
Knowledge of enterprise architecture, metadata standards, or master data management.
Experience working in agile teams or cross-functional technical environments.
Understanding of responsible AI practices and basic data ethics.

Responsibilities

Collaborate with senior data scientists to prepare, clean, and process structured and unstructured datasets for analysis.
Automate ETL workflows and streamline repetitive data preparation tasks using Python, SQL, and scripting tools.
Operate in big data ecosystems using tools such as Spark, Hadoop, or their cloud-native equivalents (e.g., AWS Glue, Azure Synapse, Databricks).
Assist in the development and deployment of data pipelines in collaboration with data engineering and DevOps teams.
Implement basic statistical analyses, visualizations, and reporting to support exploratory data analysis and hypothesis validation.
Maintain detailed documentation of data preparation methods, scripts, and pipeline configurations.
Support integration of data into downstream modeling and LLM/NLP workflows.
Contribute to operationalizing data products, APIs, and internal data access tools.
Build and maintain dashboards using Tableau or similar software.
Gain exposure to knowledge management systems supported by LLMs and assist in tagging or curating training datasets for LLM models.