ArborAR

Senior Data Scientist

Arbor
United States only
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About us:

Arbor is building the digital energy platform for modern consumers. We automatically lower our users’ home energy costs on an ongoing basis, while providing personalized insights and recommendations for energy products and services to save even more. We are backed by leading VCs, and our team has founded climate tech companies with over $2B in enterprise value. If you are looking for a rapidly scaling opportunity in consumer climate tech, let’s talk!

Role Description:

Arbor is looking for a Senior Data Scientist to join our team to supercharge the development of our data products. Data about consumers’ energy usage is fundamental to our product - we are working to bring that information to our customers in ways that help them make smarter decisions about managing their energy consumption as well as to foster understanding and engagement with their own usage behaviors. The Senior Data Scientist will be foundational to using these broad sets of data to craft those products and the user experience around them.

Responsibilities:

  • Develop data products & models to derive insights from raw energy consumption and device data that create exceptional energy user experiences

  • Deploy models at scale as part of our application following development best practices

  • Measure and evaluate the performance of product features post-launch to inform future product growth

  • Build an understanding of consumers’ needs with respect to their energy consumption and use this knowledge to devise data products that solve their needs

  • Identify and articulate business priorities and opportunities to guide new product and feature enhancements

  • Work across the organization to prioritize development and implementation of potential customer-facing features to enhance our product’s capabilities

  • Work with raw energy consumption and device data - ingest, organize, and transform this data into structures that enable downstream applications

Requirements:

  • Ability to go the full data cycle: source data inputs, build and tune a model/data product, deploy that model into a product, and measure impact to inform future development

  • Demonstrated experience implementing statistical and machine learning models in a customer facing application with proven business impact

  • High levels of competency with Python & SQL

  • Familiarity with a dbt/Snowflake data stack

  • Top notch communication and interpersonal skills

  • High level problem solving skills + ability to troubleshoot your way out of ambiguous situations

If you are passionate about solving complex problems, working cross-functionally at a fast growing company, and delighting customers, we encourage you to apply for this exciting opportunity.

Location & Work Environment:

Arbor is a fully remote company with hub locations in Washington, DC and Boston, MA. At Arbor, we deeply value the relationships and camaraderie among our team. Therefore, we periodically gather at hubs for special occasions such as team events or when welcoming new members. Employees should plan to travel to these hubs for team events.

Why you should join our team:

By joining Arbor, you’ll be a founding member of a fast-growing company in the climate tech and energy space. As you help us grow, you’ll be directly helping our customers save money and encouraging more environmentally sustainable energy choices.

We look forward to hearing from you!

At Arbor, we're looking for people with passion, grit, and integrity. You're encouraged to apply even if your experience doesn't precisely match the job description. Your skills and passion will stand out—and set you apart—especially if your career has taken some extraordinary twists and turns. At Arbor, we welcome diverse perspectives and people who think rigorously and aren't afraid to challenge assumptions. Join us.

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About the job

Apply before

May 19, 2024

Posted on

Mar 20, 2024

Job type

Full Time

Experience level

Senior

Location requirements

Hiring timezones

United States +/- 0 hours
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