Principal Data Scientist

Equinor ASA•Houston, TX

8d•Onsite

About The Position

We are seeking a Principal Data Scientist to our Houston-team in Design, Software and Data Science (DSD), part of Technology, Digital and Innovation (TDI). The role will work on key business challenges across Equinor, connecting traditional machine learning, mathematical optimization, reinforcement learning, decision intelligence and agentic AI with safe, scalable delivery in operational and business-critical settings. As a principal data scientist, you will provide professional technical leadership for highly complex tasks with strategic impact. You will combine deep hands-on expertise with enterprise perspective, sound judgement and the ability to align stakeholders around practical technical choices. Your work will help Equinor use AI responsibly to automate workflows, strengthen decisions and create measurable business value.

Requirements

Master’s or PhD in operations research, applied mathematics, computer science, data science, control/automation, engineering or a related discipline.
Solid experience and understanding of production-grade AI/ML delivery, including data pipelines, model evaluation, software engineering practices, MLOps, cloud platforms and secure deployment.
Ability to turn ambiguous business questions into analytical problems, delivery plans, decision recommendations and measurable outcomes.
Strong professional depth in machine learning, mathematical optimisation, reinforcement learning, decision science, or advanced AI, with demonstrated ability to solve highly complex tasks.
Experience leading technical tasks or work packages involving several contributors, stakeholders and dependencies, preferably in industrial, operational or enterprise-scale environments.
Strong stakeholder management and communication skills, with the ability to explain advanced technical concepts, influence across disciplines and facilitate decisions.
Sound judgement on risk, safety, ethics, compliance and commercial value when applying AI to business-critical processes.
Domain understanding from energy production, logistics, power systems, process control, maintenance, trading, project execution or other complex industrial settings.

Nice To Haves

Experience with optimisation solvers, stochastic or robust optimisation, Bayesian optimisation, multi-objective trade-offs, uncertainty quantification or sequential decision-making.
Experience combining optimisation or reinforcement learning with GenAI or agentic systems for planning, orchestration, scenario exploration or decision support.
Experience with Azure, Databricks, Kubernetes, Python, CI/CD, MLflow or similar technologies used in scalable AI delivery.
Retrieval and knowledge systems, including vector databases, enterprise search and knowledge graphs.

Responsibilities

Drive complex AI deliveries from problem framing to implemented solution, including scope, priorities, delivery approach, technical quality and stakeholder alignment.
Provide professional support to address key business issues, connecting optimization, reinforcement learning, forecasting and agentic AI to practical decisions in areas such as operations, engineering, energy markets, supply chain and maintenance.
Manage uncertainty and non-deterministic behavior by designing for bounded autonomy, confidence signaling, human oversight, auditable decisions, safe fallback and rollback paths.
Improve performance, reliability and cost efficiency across development, evaluation and inference, using techniques such as batching, caching, scalable serving, model routing, quantization and fit-for-purpose model selection.
Where relevant, combine agentic AI with mathematical optimization, reinforcement learning, Bayesian optimization or adaptive experimentation to support planning, scheduling, control, scenario exploration and decision support.
Coach and advise colleagues on highly complex AI topics, helping teams handle ambiguity, adopt new technology and improve digital and analytical maturity.