Time Series Researcher – Forward Deployed

Applied Computing

. Own Orbital’s foundational time-series modelling stack .

Posted 9/28/2026full-timeHouston • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in time-series modelling, deep learning, and probabilistic modelling, with a strong focus on deploying models in real-world systems. Proficient in designing uncertainty-aware models and integrating classical and modern approaches for forecasting and anomaly detection.

Highest-signal resume keywords

PhD In Computer ScienceDeep Learning & Probabilistic ModellingExpert Python SkillsProduction-Grade PyTorch CodeTime-Series Modelling Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Time-Series ModellingForecastingAnomaly DetectionStatistical ModelsDeep LearningProbabilistic ModellingUncertainty EstimationModel DeploymentCI/CD PipelinesBack-Testing Protocols

Tools & Technologies

DockerAWSAzureEKSECSSageMaker

Industry Keywords

Signal ProcessingPhysics-Informed MLReal-Time InferenceContinuous Production RetrainingHybrid Approaches

Tech Stack

Tools & technologies

AWSAzureDockerPythonPyTorch

About the role

Key responsibilities & impact

Own Orbital’s foundational time-series modelling stack
Design, validate, and deploy foundational time-series models under real-world constraints
Work with customer engineering teams to fine-tune models for their plants
Own the full lifecycle from theoretical formulation and experimentation to real-time inference, uncertainty estimation, and continuous production retraining
Design architectures for forecasting, classification/anomaly detection, and optimisation/control-adjacent tasks
Develop hybrid approaches combining classical statistical models, deep learning, and physics-based constraints
Integrate conservation laws, process constraints, and differential-equation-based priors
Design uncertainty-aware models and quantify confidence under sensor drift, failures, regime changes, and sparse or delayed ground truth
Containerise and deploy models using Docker on AWS/Azure, including EKS, ECS, and SageMaker
Build or integrate CI/CD pipelines for training, evaluation, rollout, rollback, and automated retraining
Define back-testing and evaluation protocols and build automated benchmarking pipelines
Compare classical baselines with modern deep-learning approaches
Ensure model claims are defensible to customers, partners, and internal stakeholders

Requirements

What you’ll need

PhD in Computer Science, Statistics, Applied Mathematics, Physics, or related field
First-author publications in time-series modelling, forecasting, signal processing, or physics-informed ML
3+ years of hands-on research experience in time-series or sequence modelling
Demonstrated experience in Deep Learning & Probabilistic modelling
Expert Python skills with production-grade PyTorch code
Experience deploying ML models into real systems