Machine Learning & Modeling Intern

Form Energy

380

Somerville, MA, United States

Posted on: 2022-11-01

Category: renewables

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Expired

Employment type:

Internship

Experience required:

Intern

Salary

Salary not provided

About the company:

Form Energy is developing a new class of cost-effective, multi-day energy storage systems that will enable a reliable and fully-renewable electric grid year-round. Our pioneering battery technology will entirely reshape the global electric system and give it new form.

Learn more about Form Energy at https://www.formenergy.com.

We’re always looking for outstanding people to join our team. If you’re interested, please visit https://formenergy.com/careers/.

Are you ready to rise to the challenge of climate change with the team that will deliver? Form Energy is a U.S. technology and manufacturing company that is developing and commercializing pioneering energy storage technologies to enable the electric grid to run on 100% renewable energy, every day of the year.

Supported by leading investors such as Breakthrough Energy Ventures, ArcelorMittal, TPG Rise, MIT’s The Engine, and others, we share a common belief that low-cost, multi-day energy storage is the key to enable tomorrow’s zero carbon electric grid. Driven by our core values of humanity, excellence, and creativity, we are deeply motivated and inspired to create a better world. We need talented, hardworking individuals who share our goal of tackling the challenge of climate change. Do you want to work with us today to build a better tomorrow?

Role Description

Form Energy is hiring a Machine Learning and Modeling Intern as part of our Battery Products team. In this role, you will perform data analysis, extract insights from data, and build predictive machine learning models of battery performance, based on battery test data. Your work will contribute to improving the design and operation of our batteries to maximize battery performance throughout the whole battery product lifetime. You will be part of a highly collaborative and cross functional team, and have the opportunity to grow your technical and communication skills while helping tackle climate change.

Besides joining a community of people working to make the world better, Form Energy commits to you equitable compensation, stock options, and offers a generous benefits package to make sure you have the support you need to thrive.

We cover 100% of employee premiums and 80% of dependent premiums for medical, dental, and vision insurance for full time employees. We offer a flexible Paid Time Off program and every employee, regardless of gender identity or expression, is eligible for 12 weeks of paid parental bonding leave. A full listing of our benefits is available on our careers page.

At Form Energy, we are working toward a 100% renewable energy future for everyone in the world. We are committed to creating an inclusive environment for all our employees and are seeking to build a team that reflects the diversity of the people we hope to serve with our revolutionary products. Form Energy is proud to be an equal opportunity employer.

What You'll Do:

Perform data wrangling, visualization and exploratory data analysis on battery test data to extract insights on key variables governing battery performance.

Build and test machine learning models to predict battery performance and degradation metrics.

Opportunities to collaborate closely with battery test engineers and electrochemical engineers to analyze results in the context of battery optimization.

Opportunities to present results to a broad technical audience.

Document approaches and methods as a reference for other data scientists and engineers in the company.

What You'll Bring:

Pursuing a graduate-level degree in data science, engineering, or a related quantitative field.

Extensive experience in Python, including pandas, scikit-learn, keras, tensorflow, or similar libraries.

Experience with building machine learning and/ or deep learning models using clustering algorithms, regression, decision trees, random forests, ANNs, CNNs, and related techniques.

Familiarity with git and programming environments such as VSCode, PyCharm, Jupyter notebooks, Colab or similar, preferred.

Strong communication, teamwork and organizational skills.

Interest in learning about batteries and energy storage technologies. (Prior battery experience is not required.)

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