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GE Vernova · 3 weeks ago

Sr Staff Data Scientist

Greenville

Full-time

Hybrid

Senior Level, Lead/Staff

$145K/yr - $217K/yr

8+ years exp

GE Vernova is a leader in delivering high-impact Data Science and Machine Learning solutions for industrial operations. The Sr Staff Data Scientist will lead teams to translate operational challenges into predictive solutions, focusing on time-series forecasting and anomaly detection across various energy sectors.

EnergyEnergy EfficiencySustainability

Responsibilities

Collaborate with business/domain leaders to identify, prioritize, and scope high-value ML use cases (e.g., time-series forecasting, anomaly detection, predictive maintenance), define success metrics, and ensure measurable business impact

Lead and oversee the end-to-end DS/ML lifecycle: data acquisition, cleaning, feature engineering, and exploratory analysis for industrial datasets (sensor/telemetry, production logs, emissions, maintenance history)

Develop, validate, and tune models across regression, classification, time-series (ARIMA/Prophet/LSTM/GRU/state-space), anomaly detection, and ensembles; apply deep learning when appropriate; ensure robust cross-validation and reproducibility

Deploy models to production on cloud platforms (AWS/Azure/GCP); guide choices for model serving, latency, throughput, and scalability; Own and influence the ML systems architecture, including model lifecycle management, feature pipelines, CI/CD for ML, observability, drift detection, and retraining strategies; partner with platform teams to define scalable and compliant ML-Ops patterns

Partner with data/platform engineering to operationalize pipelines and integrate models into business applications and workflows; ensure reliability, observability, and SLAs

Establish and champion standards, reusable assets, and best practices for data quality, governance, security-by-design, and validation across programs

Mentor and coach data scientists/analysts; perform code/model reviews; grow skills and foster a strong data science culture; lead small teams/projects with moderate risk and complexity

Translate model outcomes into clear, actionable insights for technical and non-technical stakeholders; communicate trade-offs, risks, and assumptions; quantify value realization

Collaborate with Reliability Engineering to apply reliability analytics (e.g., Weibull analysis, survival/hazard models, RGA/Crow-AMSAA), integrate CMMS/EAM/APM and historian/SCADA data, and inform maintenance and spares strategies where applicable

Stay current with advancing ML methods (especially industrial IoT analytics, streaming/real-time) and evaluate/pilot GenAI/LLM-assisted workflows (e.g., analytics automation, documentation, knowledge retrieval) as an added advantage

Contribute to functional data/analytics strategy and roadmaps; influence cross-functional ways of working; ensure alignment with GE Vernova standards and compliance requirements

Qualification

PythonSQLTime-series forecastingAnomaly detectionPredictive maintenanceCloud ML platformsDeep learningData managementCuriosityLeadershipProblem solvingCommunicationCreativity

Required

Minimum of 8 years' experience in operations, maintenance or monitoring of at least one of the industry domains: Oil & Gas, Fossil Power, Renewable Power

Bachelor's Degree in Computer Science or 'STEM' Majors (Science, Technology, Engineering and Math) with minimum 10 years of experience

Expert proficiency in Python and SQL; strong in libraries such as Pandas, NumPy, scikit-learn; experience with TensorFlow/PyTorch where deep learning is applicable

Advanced time-series and anomaly detection for industrial data; predictive maintenance modeling and feature engineering for sensor/telemetry and maintenance data

Cloud ML platforms (e.g., AWS SageMaker, Azure ML, GCP Vertex AI), CI/CD for ML, model registries, monitoring and drift detection; design for scalable, reliable serving

Data management practices at scale: data quality and cleansing strategies, governance and security controls, and fit-for-purpose data/feature architectures for ML

15 Years of overall experience in Data Science and Analytics field with minimum 8 years' experience in operations within at least one of: Oil & Gas, Fossil Power, Renewable Power

Strong business understanding: align analytical solutions to P&L priorities and operational KPIs (availability, MTBF/MTTR, throughput, energy yield, emissions, cost)

Leads small teams/projects; attracts, mentors, and develops talent; establishes best practices and reusable patterns; builds trust and consensus across functions

Advanced problem solving: prioritizes, removes roadblocks, and aligns solutions to organizational objectives; introduces new perspectives to existing solutions

Strong written and verbal communication: crafts compelling narratives tailored to technical and non-technical audiences; coaches others on effective storytelling

Preferred

Master's/PhD preferred

Generative AI as a value-adding plus

Stay current with advancing ML methods (especially industrial IoT analytics, streaming/real-time) and evaluate/pilot GenAI/LLM-assisted workflows as an added advantage