Data Science Engineer

Digantara U.S. is a leading Space Surveillance and Intelligence company focused on ensuring orbital safety and sustainability. With expertise in space-based detection, tracking, identification, and monitoring, Digantara provides comprehensive domain awareness across regimes, allowing end users to have actionable intelligence on a single platform.

At the core of its infrastructure lies a sophisticated integration of hardware and software capabilities aligned with the key principles of situational awareness: perception (data collection), comprehension (data processing), and prediction (analytics). This holistic approach empowers Digantara to monitor all Resident Space Objects (RSOs) in orbit, fostering comprehensive domain awareness.

Digantara U.S. is seeking an experienced and driven Data Science Engineer to develop the machine-learning components and integration infrastructure supporting the company's SDA analytics pipelines. Across programs, the role builds trajectory-classification and anomaly-detection models that operate on orbit-determination output, and maintains the benchmarking and evaluation frameworks that establish whether those pipelines perform under sparse, gapped, and noisy observation conditions.

Emphasis is placed on the characteristics that determine operational value false-positive behavior under degraded observations, calibration of confidence metrics suitable for operator use, and inference cost compatible with constrained onboard processing. The role works closely with astrodynamics and embedded-systems staff to ensure models reflect genuine dynamical structure and remain deployable within onboard resource limits.

• Be part of a collaborative, innovative and mission-focused environment where your ideas and skills have significant, real-world impact.
• Shape company culture, set the foundation for future success, build world-class teams.
• Competitive salary, benefits and equity package.

• Develop AI/ML models for trajectory classification across orbit regimes and families, and for detection of anomalous dynamical behavior.
• Integrate and maintain end-to-end analytical pipelines spanning observation processing, hypothesis generation, orbit estimation, propagation, and classification; define interfaces, own the shared, reproducible codebase.
• Build benchmarking and evaluation frameworks: estimator convergence behavior, classification accuracy and confusion structure, false-positive and false-negative characterization, time-to-custody, and sensitivity to track gaps and elevated measurement uncertainty.
• Design experiments that distinguish genuine generalization from dataset artifacts 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 operator decisions.
• Partner with embedded-systems staff to characterize model complexity, memory footprint, and inference latency, and to identify quantization, pruning, or architectural simplifications that perform within deployment constraints.
• Contribute machine-learning inputs to CONOPS and systems‑engineering activities, including data-flow definition, model lifecycle and retraining considerations, and critical technology element identification.

• BS/MS in Computer Science, Applied Mathematics, Statistics, Aerospace Engineer, Physics, or a related quantitative field, plus [4]+ years of applied ML experience (or equivalent).
• Strong Python and the scientific stack (Num Py, Sci Py, pandas); fluency in at least one deep-learning framework (PyTorch preferred).
• Demonstrated experience building ML systems on time-series, sequential, or…