Sr Staff Data Scientist

Job Description Summary

Verantwortlich für das Entwerfen, Erstellen, Bereitstellen und Warten von Softwareanwendungen und -diensten. Arbeiten in den Bereichen Maschine, Cloud, Plattformen und/oder Anwendungen. Verantwortlich für den gesamten Software-Lebenszyklus einschließlich Aktivitäten wie Anforderungsanalyse, Dokumentation/Verfahren und Implementierung.

Job Description

Hybrid role: in office

Roles and 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.

• Bachelor's Degree in Computer Science or "STEM" Majors (Science, Technology, Engineering and Math) with minimum 10 years of experience.
• Master's/PhD preferred.

• 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.
• 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; ability to translate operational realities (failure modes, maintenance strategies, process constraints) into features, validation criteria, and deployment constraints.
• 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.