Battery Data Scientist

Fluence

1010

Bangalore, India, India

Posted on: 2024-09-20

Category: energy

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Employment type:

Full time

Experience required:

Intermediate

Salary

Salary not provided

About the company:

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.

The Battery Data Scientist will contribute to all aspects of the development of Battery Analytics products. This includes the development of data pipeline, troubleshooting battery analytics algorithms and their validation, and design and implement AI-powered advanced 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, especially with statistical nature.

•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, Electrical and Computer 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 Balance (SOB), State of Energy (SOE), SOx, Capacity) estimation, battery safety, internal cell temperature and thermal gradients, internal resistance, balancing, Round Trip Efficiency (RTE), 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.

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