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Senior Data Scientist

Systems Enginuity · Remote, US

Spotted 40m agoFull-time

Job details

Pay
$95,000 – $112,000 a year
Work mode
Hybrid
Employment
Full-time
Level
Senior
Experience
5+ years
Education
Bachelor's degree
Posted
Oct 11, 2026
Last confirmed open
Oct 11, 2026
Job description

About this role

Position Summary

Systems Enginuity, Inc. (SEI) is seeking a Senior Data Scientist / Machine Learning Engineer to research, develop, and evaluate advanced analytical and predictive modeling approaches using aircraft trajectory, operational, environmental, and related aviation data.

Supporting FAA aviation safety initiatives, the successful candidate will investigate methods for trajectory prediction, anomaly detection, precursor identification, and spatial temporal analysis to identify patterns and conditions associated with emerging safety risk.

In this role, you will evaluate both established and emerging statistical and machine learning techniques, develop and assess trajectory-based features and indicators, and rigorously compare candidate models based on predictive performance, warning lead time, false alert behavior, uncertainty, robustness, and operational usefulness.

You will work closely with aviation researchers and data engineers to transition promising analytical approaches from research and experimentation into near real-time applications.

Key Responsibilities

  • Research, develop, and evaluate statistical, machine learning, and deep learning approaches for trajectory prediction, anomaly detection, precursor identification, and spatial temporal safety analysis.
  • Investigate and compare candidate modeling approaches, including conventional machine learning, probabilistic methods, neural networks, sequence/ time-series models, transformers, graph-based methods, and hybrid physics/ML approaches.
  • Develop and evaluate trajectory features, behavioral patterns, spatial temporal relationships, and precursor indicators for abnormal or emerging safety conditions.
  • Train, test, validate, and compare candidate models using measures of predictive performance, warning lead time, false positive/false negative behavior, robustness, calibration, and generalizability.
  • Evaluate existing trajectory prediction and analytical methods to determine opportunities for reuse, adaptation, or enhancement within aviation safety applications.
  • Partner with data engineers to transition analytical methods from historical data research into near real-time prototype systems.
  • Develop interpretable model outputs, uncertainty estimates, and confidence measures suitable for aviation safety analysis and oversight.
  • Document model assumptions, experimental methods, results, limitations, and recommendations to support reproducibility and technical review.

Required Qualifications

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Statistics, Applied Mathematics, Engineering, or a related quantitative field.
  • Professional experience applying machine learning, data science, predictive modeling, or research methods:
  • 3+ years with a Master’s degree, or
  • 5+ years with a Bachelor’s degree.
  • Strong Python programming skills and experience with common ML/scientific computing frameworks such as PyTorch, TensorFlow, Scikit Learn, or similar tools.
  • Demonstrated experience developing, validating, and comparing predictive models using large and complex datasets.
  • Strong understanding of model evaluation, cross validation, feature engineering, overfitting/generalization, uncertainty quantification, and performance measurement.
  • Experience with time-series, telemetry, geospatial, spatial temporal, or other multidimensional data.

Preferred Qualifications

  • Experience with trajectory prediction, anomaly detection, sequence models, Transformers, GNNs/graph-based learning, probabilistic modeling, or hybrid physics/ML approaches.
  • Familiarity with model deployment accelerators (TensorRT, ONNX) and experience converting machine learning models into high-performance execution graphs for real-time applications.
  • Aviation, aerospace, autonomous systems, robotics, transportation, or safety critical analytics experience

Our Interview Process

At SEI, we believe the interview process should be personal, transparent, and authentic. We do not use AI to conduct interviews. Selected candidates will meet directly with SEI Project Engineers and Hiring Managers in live online interviews to discuss qualifications, experience, and potential fit within our team and culture.

To ensure a fair evaluation process, we ask candidates not to use AI tools or other assistance during interviews. We evaluate each candidate based on their own knowledge, skills, experience, and problem-solving abilities.

Eligibility Requirements

Candidates who receive and accept an offer of employment with SEI must successfully complete a U.S. Federal Government public trust security process, which includes fingerprinting, a government conducted background investigation, and other required screenings.

Due to the requirements of the FAA contract supporting this position, applicants MUST BE a U.S. citizen or lawful PERMANENT resident and eligible to satisfy applicable U.S. Federal Government suitability and security requirements. SEI is unable to provide employment visa sponsorship for this position.

Individuals working under or requiring temporary employment authorization, including F 1 CPT, OPT, or STEM OPT, are not eligible.

Pay: $95,000.00 - $112,000.00 per year

Benefits:

  • 401(k)
  • 401(k) matching
  • Dental insurance
  • Flexible schedule
  • Health insurance
  • Paid time off
  • Parental leave
  • Retirement plan
  • Vision insurance

Work Location: Remote

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