Senior Data Scientist- Machine Learning

Fluence is the leading global energy storage technology and services company, created and backed by Siemens and AES, two industry powerhouses and pioneers in energy storage. Fluence unites the scale, experience, breadth, and financial backing of the two most experienced icons in energy storage.

Our mission is to create a more sustainable future by transforming the way we power our world. Energy storage is critical to this transformation, yet today the market is fragmented and customers face the challenge of finding a trusted technology partner amidst conflicting technical claims, inexperienced vendors and installers, and new market entrants with limited power sector knowledge.

Fluence brings the proven technology solutions and services that overcome the commercial and regulatory barriers that stand in the way of modernizing our energy networks. We are the partner that can deliver at a global scale with the most experienced and knowledgeable team in the world.

This position will be within the Fluence Digital business unit, formed following Fluence’s acquisition of San Francisco-based start-up AMS. Fluence Digital’s software technology uses artificial intelligence, advanced price forecasting, portfolio optimization and market bidding to ensure energy storage and flexible generation assets are responding optimally to market dynamics.

As a Senior Data Scientist – Machine Learning, you will provide technical expertise and collaborate with cross-functional teams to advance the forecasting capabilities within our market-leading optimization and bidding product, Mosaic, designed for renewable energy and battery storage assets in global electricity markets. You will identify impactful initiatives, address challenges proactively, and drive key projects to successful completion. In addition, you will contribute to mentoring and supporting the growth of team members within the Forecasting team.

Key responsibilities include:

•Forecasting Model Development: Design, develop, deploy, and maintain advanced statistical and machine learning models for time series forecasting, with applications in demand, price, and renewable energy prediction. Incorporate probabilistic scenario generation to characterize uncertainty in forecasts.

•Advanced Forecasting Techniques: Leverage ensemble-based forecasting methods and develop self-correcting models of models that iteratively improve forecast accuracy by dynamically integrating feedback and error correction mechanisms.

•Uncertainty Characterization: Implement stochastic processing models to generate probabilistic forecast scenarios that characterize uncertainty in time series data.

•Multivariate Time-Series Modeling and Dependence Structure Analysis: Implement advanced stochastic modeling frameworks to capture the dependence structure of temporal and spatial correlations in time-series data across a sequence of markets, ensuring realistic scenario generation that reflects the complex relationships and dynamics within and between markets.

•Analyzing Electricity Market Dynamics: Conduct in-depth analyses of electricity market trends to decipher the evolution of market forces and identify shifting dynamics that influence price formation for energy and ancillary services. Leverage this understanding to refine forecasting models and strategic decision-making.

•End-to-End Workflows: Architect and manage workflows for feature engineering, model training, and inference using modern orchestration tools (e.g., Argo Workflows, AWS Step Functions).

•Model Deployment: Create, deploy, and optimize containerized solutions (e.g., Docker images) for scalable training and inference environments. Utilize Kubernetes and serverless platforms (e.g., AWS Lambda) for efficient model deployment.

•Data Engineering: Design robust ETL pipelines and define database schemas to streamline data ingestion, preprocessing, and storage, ensuring seamless integration into forecasting workflows.

•Infrastructure and Automation: Implement CI/CD pipelines for model and code deployment, ensuring repeatable, automated workflows for version control, testing, and production rollouts (e.g., CircleCI).

•Performance Monitoring and Optimization: Monitor deployed models and workflows to ensure reliability, scalability, and optimal performance. Continuously refine and improve systems to meet evolving requirements.

•Collaboration: Work closely with cross-functional teams, including data scientists, engineers, and product stakeholders, to ensure smooth integration of forecasting systems into broader applications.

What will our ideal candidate bring to Fluence? 

•Experience: 5+ years of industry experience developing forecasting models, preferably for energy systems.

•Time Series Expertise: Strong understanding of time series forecasting techniques, including statistical models, machine learning algorithms, and deep learning approaches.

•Uncertainty Characterization: Expertise in implementing stochastic processing models to generate probabilistic forecast scenarios that characterize uncertainty in time series data.

•Multivariate Time-Series Modeling: Experience developing stochastic frameworks that capture temporal and spatial correlations across markets, ensuring realistic and robust scenario generation.

•Advanced Forecasting Techniques: Familiarity with ensemble-based methods and self-correcting forecasting models that improve through iterative feedback.

•Forecasting Experience: 7+ years of industry experience developing forecasting models, preferably to address bidding and dispatch challenges in electricity markets.

•Energy Industry Knowledge: Comprehensive knowledge of the energy industry, with a focus on deregulated electricity markets such as NEM, CAISO, ERCOT, MISO, PJM, and Japan.

•Programming and Software Practices: Advanced proficiency in Python for feature engineering, model development, training, and forecasting, with strong knowledge of modern software development practices, including version control (Git), testing frameworks, and agile methodologies.

•Cloud and Infrastructure: Proficiency with AWS services (e.g., SageMaker, Lambda, Step Functions), containerization tools like Docker, container orchestration using Kubernetes, and managing workflows for large-scale data and model pipelines.

•ETL and Data Management: Proven experience in designing ETL pipelines, creating efficient data schemas, and managing data flows at scale.

•CI/CD Pipelines: Hands-on experience with continuous integration and delivery tools to automate and streamline model and software deployment.

•Agile/Lean Product Development: Experience working and delivering products or services in an agile/lean environment, demonstrating adaptability and efficient collaboration.

•Collaboration Skills: Demonstrated ability to collaborate with cross-functional teams and build strong working relationships across disciplines.

•Communication Skills: Excellent communication skills, with the ability to articulate technical concepts clearly and effectively to diverse stakeholders.

•Educational Background: An advanced degree (Master’s or PhD) in Computer Science, Operations Research, Electrical Engineering, Mathematics, Statistics, or a related field.