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.

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OUR CULTURE AND VALUES

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.

We are Leading

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.

We are Responsible

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

We are Agile

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

We are Fun

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.

Job Description

The Battery Data Scientist will contribute to all aspects of the development of Battery Analytics products. This includes but is not limited to the development of data pipeline, troubleshooting 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

•Acts 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.

•Uses coding and data analysis to derive data-driven decisions regarding battery systems.

•Effectively collaborates and communicates with the team members.

Required Experience and Skills

•Bachelor's or master’s in computer science (or related fields/experience) with/or background/passion in Data Science, Statistics, Data Engineering, Data Mining, ML Operations (MLOps), and related fields.

•Fast learning and implementing new algorithms 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.

Desired Experience and Skills

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

•Familiarity with data acquisition systems.

•3 to 5 years of industrial experience.

•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.

•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.