Description

POSITION DESCRIPTION:

We are seeking a highly skilled Senior Data Scientist with deep expertise in time series forecasting and MLOps to join our Grid Energy & Analytics team. This role sits at the intersection of statistical modeling, machine learning, and production of ML systems, with a strong emphasis on building reliable, scalable forecasting solutions that operate in real-world, high-stakes environments.

The ideal candidate is a hands-on technical leader who can own forecasting problems from data preprocessing and feature engineering to model development, deployment, monitoring, and lifecycle management. You will play a key role in shaping our forecasting platform, improving model performance at scale, and establishing best-in-class MLOps practices that enable rapid experimentation while ensuring production reliability and governance on scale.

RESPONSIBILITIES

Times Series Forecasting & Modeling

• 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.

MLOps & Production ML Systems

• 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.

Research, Collaboration, and Domain Knowledge Application

• 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.

REQUIRED QUALIFICATIONS

• 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.

PREFERRED QUALIFICATIONS

• 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.

PHYSICAL, MENTAL & ENVIRONMENTAL DEMANDS:

To comply with the Rehabilitation Act of 1973 the essential physical, mental and environmental requirements for this job are listed below. These are requirements normally expected to perform regular job duties. Incumbent must be able to successfully perform all of the functions of the job with or without reasonable accommodation.

Mobility

Standing

20% of time

Sitting

70% of time

Walking

10% of time

Strength

Pulling

up to 10 Pounds

Pushing

up to 10 Pounds

Carrying

up to 10 Pounds

Lifting

up to 10 Pounds

Dexterity (F = Frequently, O = Occasionally, N = Never)

Typing

F

Handling

F

Reaching

F

Agility (F = Frequently, O = Occasionally, N = Never)

Turning

F

Twisting

F

Bending

O

Crouching

O

Balancing

N

Climbing

N

Crawling

N

Kneeling

N

The salary range is required by the California Pay Transparency Act and may differ depending on the location of those candidates hired nationwide. Actual compensation is influenced by a wide array of factors including but not limited to, skill set, education, licenses and certifications, essential job duties and requirements, and the necessary experience relative to the job's minimum qualifications.

*This target salary range is for CA positions only and should not be interpreted as an offer of compensation.