Data Science Engineer (1144734)

The Judge Group; Westminster (CO); On-site; USD 90,000 - 140,000; Full time

Job summary

The Judge Group is seeking a Data Science Engineer to develop ML components and the integration infrastructure that support space-domain awareness analytics pipelines. On-site in Westminster, CO you will build trajectory-classification and anomaly-detection models that operate on orbit-determination output.

You will also build benchmarking and evaluation frameworks to ensure pipeline performance under sparse, noisy observations while collaborating with astrodynamics and embedded-systems teams to

Qualifications

• BS or MS in Computer Science, Applied Mathematics, Statistics, Aerospace Engineering, Physics, or a related quantitative field.
• 4+ years of applied machine learning experience (or equivalent).
• Strong proficiency in Python and the scientific stack (NumPy, SciPy, pandas).
• Fluency in at least one deep-learning framework (PyTorch is strongly preferred).
• Demonstrated experience building ML systems on time-series, sequential, or state-estimation-adjacent data, rather than just tabular or vision benchmarks.
• Sound understanding of evaluation methodology, including class imbalance, calibration, uncertainty quantification, and the failure modes of small or synthetically generated datasets.
• Strong software-engineering discipline sufficient for a shared codebase, including version control, testing, reproducible environments, and documented interfaces.
• Clear technical writing skills for producing high-quality customer-facing deliverables.
• Must be able to obtain and hold a U.S. security clearance.

Responsibilities

• Develop AI/ML models for trajectory classification across various orbit regimes and families, and for the detection of anomalous dynamical behavior.
• Integrate and maintain end-to-end analytical pipelines spanning observation processing, hypothesis generation, orbit estimation, propagation, and classification.
• Define system interfaces and take full ownership of a shared, reproducible codebase.
• Build benchmarking and evaluation frameworks to measure estimator convergence behavior, classification accuracy and confusion structure, false-positive/negative characterization, time-to-custody, and sensitivity to track gaps and elevated measurement uncertainty.
• Design experiments that distinguish genuine model generalization from dataset artifacts—including held‑out families, degraded-observation ablations, and cross‑checks against independent reference datasets.
• Produce calibrated confidence metrics suitable for downstream operational use, documented precisely enough to support critical operator decisions.
• Partner with embedded‑systems staff to characterize model complexity, memory footprint, and inference latency; identify quantization, pruning, or architectural simplifications that meet deployment constraints.
• Contribute machine-learning expertise to CONOPS and systems‑engineering activities, including data‑flow definition, model lifecycle and retraining considerations, and the identification of critical technology elements.

Skills

Python

NumPy

SciPy

pandas

PyTorch

Time-series ML

Software engineering

Education

Bachelor's/Master's in CS/Applied Math/Statistics/Aerospace/Physics or related field

Tools

Git

Testing

Reproducible environments

Job description

Location: Westminster, CO

Salary: $100,000.00 USD Annually - $140,000.00 USD Annually

Salary Range: $90K-$110K

Our client is currently seeking a Data Science Engineer

Location: Onsite in Westminster, CO

About The Role

Our client is an innovative and mission-focused organization seeking an experienced, driven Data Science Engineer. In this role, you will develop the machine-learning components and integration infrastructure that support the company's space domain awareness (SDA) analytics pipelines.

Across various programs, you will build trajectory-classification and anomaly-detection models that operate on orbit-determination output. You will also build and maintain the benchmarking and evaluation frameworks necessary to ensure these pipelines perform accurately under sparse, gapped, and noisy observation conditions.

A strong emphasis is placed on characteristics that determine real-world operational value: managing false-positive behavior under degraded observations, calibrating confidence metrics suitable for operator use, and ensuring inference costs remain compatible with constrained onboard processing.

You will work closely with astrodynamics and embedded-systems teams to ensure that models reflect genuine dynamical structures and can be successfully deployed within strict onboard resource limits.

Why Join Us?

• Impact: Be part of a collaborative, innovative, and mission-focused environment where your ideas and skills have a significant, real-world impact.
• Growth: Help shape the company culture, set the foundation for future success, and build world‑class teams.
• Compensation: Enjoy a competitive salary, comprehensive benefits, and an attractive equity package.

Responsibilities

• Develop AI/ML models for trajectory classification across various orbit regimes and families, and for the detection of anomalous dynamical behavior.
• Integrate and maintain end-to-end analytical pipelines spanning observation processing, hypothesis generation, orbit estimation, propagation, and classification.
• Define system interfaces and take full ownership of a shared, reproducible codebase.
• Build benchmarking and evaluation frameworks to measure estimator convergence behavior, classification accuracy and confusion structure, false-positive/negative characterization, time-to-custody, and sensitivity to track gaps and elevated measurement uncertainty.
• Design experiments that distinguish genuine model generalization from dataset artifacts—including held‑out families, degraded-observation ablations, and cross‑checks against independent reference datasets.
• Produce calibrated confidence metrics suitable for downstream operational use, documented precisely enough to support critical operator decisions.
• Partner with embedded‑systems staff to characterize model complexity, memory footprint, and inference latency; identify quantization, pruning, or architectural simplifications that meet deployment constraints.
• Contribute machine-learning expertise to CONOPS and systems‑engineering activities, including data‑flow definition, model lifecycle and retraining considerations, and the identification of critical technology elements.

Minimum Qualifications

• BS or MS in Computer Science, Applied Mathematics, Statistics, Aerospace Engineering, Physics, or a related quantitative field.
• 4+ years of applied machine learning experience (or equivalent).
• Strong proficiency in Python and the scientific stack (NumPy, SciPy, pandas).
• Fluency in at least one deep-learning framework (PyTorch is strongly preferred).
• Demonstrated experience building ML systems on time-series, sequential, or state-estimation-adjacent data, rather than just tabular or vision benchmarks.
• Sound understanding of evaluation methodology, including class imbalance, calibration, uncertainty quantification, and the failure modes of small or synthetically generated datasets.
• Strong software-engineering discipline sufficient for a shared codebase, including version control, testing, reproducible environments, and documented interfaces.
• Clear technical writing skills for producing high-quality customer-facing deliverables.
• Must be able to obtain and hold a U.S. security clearance.

Preferred Qualifications

• Experience with physics-informed ML or hybrid approaches that embed dynamical structure into learned models.
• Familiarity with orbit determination, tracking, or multi-target data association (e.g., JPDA, MHT, or similar).
• Prior experience with model compression, quantization, or deployment to constrained and embedded targets.
• Prior work on government R&D programs (SBIR/STTR, AFRL, DARPA, Space Force) and familiarity with Technology Readiness Level (TRL) terminology.
• Experience with anomaly detection in environments where anomalies are rare, poorly labeled, or defined purely by a physical model.