[Remote] Sr. Data Scientist, Energy Systems

Note: The job is a remote job and is open to candidates in USA. Qcells North America is a leading photovoltaic manufacturer seeking a highly skilled Senior Data Scientist to join their Grid Energy & Analytics team. The role focuses on developing reliable forecasting solutions using statistical modeling and machine learning, while also establishing best practices for MLOps in production environments.

Responsibilities

• Design, develop, and deploy high‑quality time series forecasts for energy market prices, ancillary services, energy demand, and renewable generation (e.g., solar PV and wind)
• Apply a broad range of forecasting methodologies, including classical statistical models, machine learning, and deep learning approaches, selecting methods appropriate to data regimes and business constraints
• Lead feature engineering efforts incorporating calendar effects, weather signals, exogenous drivers, and regime changes
• Establish rigorous model evaluation, backtesting, and benchmarking frameworks to ensure accuracy, robustness, and stability over time
• Architect, build, and maintain end‑to‑end MLOps pipelines, covering data validation, training, versioning, deployment, monitoring, and retraining
• Ensure forecasting systems are scalable, observable, and reliable in production, with clear SLAs, alerting, and rollback strategies
• Partner in the design and evolution of an internal forecasting platform that supports the full machine learning lifecycle and multi‑model production hosting
• Implement best practices for model governance, reproducibility, experiment tracking, and lineage
• Conduct applied research to identify new modeling techniques, architectures, and tooling that improve forecast accuracy, latency, and operational efficiency
• Translate research ideas into production‑ready solutions, balancing innovation with maintainability
• Influence technical roadmap decisions related to forecasting systems, data platforms, and MLOps standards
• Work closely with engineering, product, and domain experts to ensure forecasting solutions deliver measurable business and operational impact
• Incorporate energy system constraints and domain knowledge into models to ensure outputs are physically meaningful and actionable
• Support production operations by troubleshooting issues, analyzing model degradation, and continuously improving system performance

Skills

• Master's or Ph.D. in statistics, machine learning, applied mathematics, computer science, or a related quantitative field
• 1-2 years of hands‑on experience in data science or machine learning, with significant exposure to time series forecasting in production
• Strong proficiency in Python and experience writing production‑quality, maintainable code using modern software engineering practices
• Deep theoretical and practical knowledge of time series methods, including statistical, regression‑based, and deep learning approaches
• Demonstrated experience building and operating ML systems in production, including CI/CD for models, monitoring, and lifecycle management
• Experience with cloud‑hosted platforms (preferably Azure / Fabric), containerization, and distributed compute
• Proficiency with core data science and ML libraries such as pandas, numpy, statsmodels, sklearn, xgboost, lightgbm, pytorch, keras, and modern forecasting libraries (e.g., Nixtla)
• Strong problem‑solving skills, ownership mindset, and ability to operate effectively in ambiguous, real‑world environments
• Travel may be required up to 10%, depending on business needs
• Experience with energy systems, electricity markets, or infrastructure forecasting, including demand, pricing, or renewable generation
• Familiarity with power systems concepts such as unit commitment, economic dispatch, or grid constraints
• Prior experience designing or contributing to forecasting platforms or shared ML infrastructure
• Exposure to large‑scale data pipelines, streaming or batch processing, and data quality frameworks
• Experience collaborating across data science, software engineering, and operations teams in a production environment

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