Senior Data Scientist (Onsite)

Two Six Technologies•Arlington, VA

2d•$150,000 - $200,000•Onsite

About The Position

Two Six Technologies is seeking an experienced Cleared and On-site Data Scientist to join their customer success team. The team builds capabilities to identify, plan, and deliver effects in the information operations space, utilizing proprietary data, AI technology, and expertise to provide insights to US government and Fortune 50 clients. The role involves working autonomously and with subject matter experts to solve complex problems using cutting-edge technology. The candidate will interact directly with customers, acting as a technical translator for internal and external stakeholders. This position is ideal for individuals motivated by autonomy, innovation, and contributing to national security challenges by interfacing directly with end-users. The role requires the candidate to be local to the DC Metro area and willing to work up to 4 days per week at Two Six offices or on customer sites in Northern Virginia and Laurel, MD. An Active Top Secret Clearance with SCI Eligibility and a polygraph are required.

Requirements

Bachelor’s degree in Physics, Computer Science, Math, Statistics, Economics, Engineering, or similar quantitative field.
5+ years of experience in a Data Science role (or 2+ years with a PhD).
3+ years of experience with Python programming language (Intermediate-level or higher).
Experience with machine learning.
Active Top Secret (TS) Clearance with SCI Eligibility.
Ability to obtain and maintain a polygraph.
Strong understanding of statistics, quantitative social science methods, and/or machine learning and AI.
Strong written and verbal communication skills, with an ability to translate complex technical concepts into easy-to-understand explanations.
Active Top Secret Clearance with SCI Eligibility (Polygraph preferred).
Experience with code version control.
Familiarity with software development processes and tools like the Agile methodology and Jira.
Experience with agentic and generative AI.
Deployment and Operations Experience.
Working with containers (e.g., Docker) and container orchestration platforms (e.g., Kubernetes, OpenShift).
Working with Linux command line.
Working within cloud environments (e.g., AWS, Azure).
Packaging and moving software, models, and dependencies into disconnected or air-gapped networks.
Monitoring, logging, and diagnosing issues in deployed systems.

Nice To Haves

Higher degrees preferred.
Direct customer support experience.
Feature selection/engineering
Model training
Model evaluation
Supervised and Unsupervised ML
Simulation
Anomaly detection
Building agentic systems
Building tools for agentic systems
Evaluating agentic systems and/or generative AI solutions
LangChain and/or LangGraph frameworks
Model Context Protocol and Agent2Agent Protocol
Prompt engineering
Leveraging LLMs within analytical pipelines
Training or fine-tuning LLMs

Responsibilities

Work with customers, subject matter experts, and the Product team to transform abstract customer needs into concrete data science and/or engineering solutions.
Experiment with and evaluate potential solutions to develop high-quality prototypes.
Be responsible for projects that span statistical and mathematical reasoning, modeling & simulation, and software development.
Experiment with a range of data science techniques to produce insight, including machine learning, deep learning, network analysis, sampling methodologies, text analytics in multiple languages and data visualization.
Represent Two Six’s technical and methodological rigor to a range of audiences from technical novices to experts.
Design and execute experiments to evaluate the integrity of data sampling and data collection strategies.
Work on ad-hoc analyses or build prototype tools to support subject matter expert and customer success teams.
Support the installation, configuration, and upkeep of Two Six and custom capabilities on customer systems.
Scope and plan data science projects, creatively navigating technical limitations, iterating to solve hard questions, and learning quickly and independently.
Lead solutions for ambiguous tasks and complex problems, independently providing solutions that fit within the business strategy.
Propose pragmatic solutions that are as simple as possible and can be iterated upon.
Work remotely on a distributed data science team with a diverse set of skills and life experiences, while embedded in a product team as owner of data science innovations to the product.