Staff Battery Research Data Scientist

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.

About Fluence: Fluence Energy, Inc. (Nasdaq: FLNC) is a global market leader in energy storage products and services, and optimization software for renewables and storage. With a presence in over 47 markets globally, Fluence provides an ecosystem of offerings to drive the clean energy transition, including modular, scalable energy storage products, comprehensive service offerings, and the Fluence IQ Platform, which delivers AI-enabled SaaS products for managing and optimizing renewables and storage from any provider. Fluence is transforming the way we power our world by helping customers create more resilient and sustainable electric grids.

For more information, visit our website, or follow us on LinkedIn or Twitter. To stay up to date on the latest industry insights, sign up for Fluence's Full Potential Blog.

 

We are guided by our passion to transform the way we power our world. Achieving our goals requires creativity, diversity of ideas and backgrounds, and building trust to effect change and move with speed.

Fluence currently has thousands of MW of energy storage projects operated or awarded worldwide in addition to the thousands of MW of projects managed by our trading platform—and we are growing every day.

Fluence is defined by its unwavering commitment to safety, quality, and integrity.

We achieve our goals and meet our customer’s needs by cultivating curiosity, adaptability, and self-reflection in our teams.

We value the diversity in thought and experience of our coworkers and customers. Through honest, forthcoming, and respectful communications we work to ensure that Fluence is an inclusive and welcoming environment for all. 

Staff Battery Research Data Scientist

Job Description

The Battery Data Scientist will lead and contribute to all aspects of the development of Battery Analytics products. This includes but is not limited to the development of data pipeline, novel battery analytics algorithms and their validation, and design and implement AI-powered smart Battery Management Systems (BMS) products. We welcome applications from diverse multi-disciplinary battery research and data science background.

Responsibilities

•Leads as one of the battery data scientists in the team to meet immediate and long-term battery data analysis requests.

•Helps the team in agile development, test, and validate state-of-the-art estimation, prediction, and statistical inference algorithms in battery systems.

•Contributes to data pipeline requirements of energy storage systems.

•Applies (or develops if necessary) pipelines and tools to efficiently collect, clean, and prepare massive volumes of data for analysis with minimal guidance.

•Interprets results and develops insights into formulated problems within the business/customer context.

•Acquires and uses broad knowledge of innovative battery state estimation/ prediction/ fault detection methods, algorithms, and tools from the scientific literature and applies his/ her own analysis of scalability and applicability to the formulated problem.  

•Helps in developing long-term battery analytics strategy and roadmap.

•Effectively collaborates and communicates with the team members, product owners, product managers, as well as marketing and sales teams, customers, and leadership.

Required Experience and Skills

•PhD or Master’s in computer science (or related fields) and 3-5 years of experience in Artificial Intelligence (AI) and Machine Learning (ML), Data Science, Data Engineering, Data Mining, ML Operations (MLOps), and related fields.

•Working knowledge of statistics, time series forecasting, feature engineering, regression, classification, clustering, outlier detection, reinforcement learning, few shot learning, multidimensional Kalman filtering, extended Kalman filters, optimization, and causal inference.

•Staying up to date with novel AI algorithms and tools and implement them whenever required.

•Programming fluency in Python (and especially data science/visualization related packages).

•Working knowledge of AWS and ML platforms, Snowflake, and Power BI Dashboard.

•Working in agile software development cycles and version control tools such as Jira and Github.

•Strong problem-solving skills, with the ability to combine theory with empirical observation.

•Interacting effectively and in an open, ethical, and trustworthy manner with internal and external stakeholders.

•Staying proactive, self‐motivated, persistent, hands‐on, goal- oriented, and team-oriented, and work in a fast-paced, US-based, and diverse environment.

•Strong written and oral communication and ability to document technical findings.

•Willingness and desire to lead technical teams and product development team.

•Developing test plans and QA procedures for algorithm/code verification and validation

Desired Experience and Skills

•PhD or Master’s in Materials Science, Chemical Engineering, Mechanical Engineering, Power Systems or related fields with deep understanding of lithium-ion electrochemistry effects as applies to simulation and modeling with track records of peer-reviewed publications/patents.

•Peer-reviewed record of publications in machine learning and artificial intelligence peer-reviewed journals and conferences/patents.

•Experience and knowledge in Battery System State (State of Charge (SOC), State of Health (SOH), State of Functionality (SOF), State of Power (SOP), SOx, Capacity) estimation, battery safety, internal cell temperature and thermal gradients, internal resistance, balancing, accelerated testing, open circuit voltage (OCV) prediction with hysteresis, Randles equivalent circuits, single particle models, Ficks law of diffusion, age modeling, and battery life degradation algorithms.

•Ability to develop and verify physics-based and empirical battery models to support algorithm needs.

•Validation of the algorithms in Model-in-the-loop (MIL) and Hardware-in-the-loop (HIL) settings and/or a lab.

•Experience with Tableau, SQL, R, Perl, Scala, JMP, Octave, Matlab, Simulink, C++, Julia, and Go.

•Familiarity with data acquisition systems.

•Experience with firmware and embedded systems for Battery Management Systems (BMS).

•3 to 5 years of industrial experience in battery systems.