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GE Vernova · 2 days ago

Sr Staff Data Scientist

Atlanta, GA

Full-time

Onsite

Senior Level, Lead/Staff

$145K/yr - $217K/yr

8+ years exp

GE Vernova is seeking a Sr Staff Data Scientist who will lead the development of high-impact Data Science and Machine Learning solutions for industrial operations. The role involves collaborating with business leaders to identify ML use cases, overseeing the end-to-end ML lifecycle, and mentoring junior data scientists to drive measurable business outcomes.

EnergyEnergy EfficiencySustainability

No H1B

U.S. Citizen Only

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 maintenanceDeep learningCloud ML platformsCI/CD for MLData management practicesReal-time analyticsCuriosityChange agentMentoringCommunicationProblem solving

Required

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

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

Experience with 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

Real-time/streaming analytics and deployment considerations; integration into business applications and workflows

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); articulate ROI and buy vs. build trade-offs; awareness of industry trends and regulatory context

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

Consulting mindset: frames options and trade-offs, provides risk-assessed recommendations, and influences stakeholders to adopt data-driven decisions

Decision making & risk: makes informed decisions in ambiguous environments; balances performance, latency, and reliability trade-offs; promotes calculated risk-taking and learning

Change agent: plans and implements change programs, drives adoption of new methods and platforms, and partners with executives to realize value at scale

Curiosity and creativity: connects ideas across domains; simplifies complex problems; champions progression from ideas to outcomes with speed

Comfort in ambiguity: delivers with incomplete information, states assumptions clearly, and course-corrects based on feedback; manages uncertainty for self and team

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