Applied Scientist, Causal Inference for Edge Learning

Octaipipe; City Of London; On-site; GBP 110,000 - 150,000; Full time

Job summary

OctaiPipe is seeking an Applied Scientist in causal inference to own the scientific reasoning behind our data-centre decision intelligence product. You will work on root-cause inference, counterfactual evaluation, and transferable causal models across sites to reduce maintenance effort and downtime.

Collaborating with reinforcement learning scientists and ML systems engineers, you will move methods from prototype to production, with rigorous evaluation, drift monitoring, and clear communication

Qualifications

• Master’s degree or higher in statistics, ML, econometrics or related quantitative field focused on causal inference.
• Hands-on experience applying causal methods to real data: structural causal models, causal discovery, quasi-experimental designs, or counterfactual estimation.
• Strong time-series skills, including data with gaps, drift and regime changes.
• Experience with probabilistic modelling and uncertainty quantification, including calibration of confidence.
• Strong instinct for identification: spotting natural experiments and designing experiments.

Responsibilities

• Own root cause inference: explain departures from expected-healthy behaviour to identify one cause per issue from alarms, telemetry and topology.
• Develop methods that separate genuine plant faults from sensor faults, known defects, expected control actions and planned work.
• Guarantee calibrated confidence and cited evidence on every diagnosis for operators.
• Own model validity: detect when a question falls outside the operating regime and communicate uncertainty.
• Build prioritisation that improves with understanding.
• Estimate treatment effects via counterfactuals and natural experiments.
• Learn how faults unfold from historical incidents across sites.
• Refine the causal structure when data shows relationships are wrong.
• Develop causal models that transfer between heterogeneous sites in collaboration with RL scientists and ML systems engineers.
• Take methods from prototype to production with evaluation, staged rollout and monitoring for drift.
• Raise scientific rigor through evaluation, code review, and clear communication.

Skills

Causal inference

Time-series analysis

Python

Probabilistic modelling

Model deployment

Education

Master's degree or higher in statistics, machine learning, econometrics or related quantitative field

Tools

Python

NumPy

pandas

PyTorch

Job description

OctaiPipe is seeking an Applied Scientist in causal inference to own the scientific reasoning behind our data-centre decision intelligence product. You will work on root-cause inference, counterfactual evaluation, and transferable causal models across sites to reduce maintenance effort and downtime.

Collaborating with reinforcement learning scientists and ML systems engineers, you will move methods from prototype to production, with rigorous evaluation, drift monitoring, and clear communication