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# Data Science & Machine Learning Fellow

- **Company:** The Eeo
- **Location:** Washington, DC, United States
- **Salary:** $24,000.0
- **Contract:** Internship / Graduate position
- **Skills:** Artificial Neural Networks, Cyber Security, Python (Programming Language), Machine Learning, Reinforcement Learning, Pytorch, Multi-Agent Systems, Information Technology, Process Control Systems, Software Version Control
- **Published:** August 18, 2026
- **Apply:** https://www.dice.com/job-detail/ff49ef8f-f89d-44a8-9125-a7f9e8ddee87

## About the Role

* Currently pursuing an MS or PhD in Computer Science, Machine Learning, Operations Research, Applied Math, Economics, Statistics, or a related quantitative field. Returning to your MS or PhD program after the fellowship (expected graduation December 2027 or later).
 * Production-quality Python and PyTorch, with solid machine learning fundamentals.
 * Hands-on experience (coursework, research, or projects) with at least one of: reinforcement learning, mathematical optimization, simulation and modeling, or time-series forecasting.
 * Able to translate a messy real-world process into a tractable formulation (an MDP with sensible state, action, and reward, or an optimization model) and explain the modeling choice. Running pre-built models on clean benchmarks is not enough.
 * Demonstrated ability to design, implement, and evaluate experiments, with reproducible research practices (version control, testing).
 * This position requires access to information and technology subject to U.S. export controls (including DOE 10 CFR Part 810 and NRC requirements). U.S. Person status (U.S. citizen or lawful permanent resident) is required, and TNC does not provide visa sponsorship for these roles.
 * Willing and able to work on-site in Washington DC, five days a week, for the full 12-week program

## Description

* Problem formulation: translate operational processes (construction scheduling, portfolio sequencing, security operations) into well-defined modeling problems and make the case for the right approach.
 * Simulation and evaluation: build environments that faithfully represent these processes so models can be trained, evaluated, and iterated on.
 * Modeling: develop reinforcement learning, optimization, or forecasting models for schedule optimization, capital allocation under uncertainty, or anomaly detection and alert prioritization.
 * Empirical research: design rigorous experiments, keep reproducible codebases, and communicate results clearly to technical and non-technical stakeholders.
 * Production path: work with engineering on how models are served, monitored, updated, and safely overridden in production., * Deep RL: policy gradient (PPO, SAC) or value-based (DQN, IQL) methods; offline / batch RL (CQL, IQL, TD3+BC, Decision Transformer).
 * Combinatorial optimization with ML: graph neural networks for scheduling or routing, or neural combinatorial optimization.
 * Multi-agent RL (MAPPO, QMIX) or stochastic / robust optimization (CVaR-constrained, chance-constrained, distributionally robust).
 * Uncertainty quantification; a game-theory or behavioral-science perspective on decision-making.
 * MLOps for models in production: serving, monitoring, retraining, and distribution-shift detection.
 * Domain exposure: construction or infrastructure operations, energy or electricity markets, industrial control systems, or critical-infrastructure security.


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