Data Scientist

Rockerbox (View all Jobs)

United States, Remote

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Interview Process

1. Phone screen with pair programming 2. 2 hours of pre-interview prep 3. 4-hour final interview with situational questions, another pair programming, and presentation and discussion of pre-interview prep outputs.

Programming Languages Mentioned

SQL, Python


Data Scientist

Rockerbox fuels the growth of leading Direct-to-Consumer (DTC) brands such as Tula, Figs, and Burton. Our guiding principle is that no marketing organization should require a data engineering team to make data-driven decisions. We take on the technical challenges of collecting and consolidating all marketing data into a single platform to enable any marketing organization, big or small, to focus on their core strength: building their brand.

The Data Science team at Rockerbox is responsible for designing, deploying, and maintaining ML-backed features including our Media Mix modeling (MMM) product, the cornerstone of our measurement diversification efforts. Reporting to the Head of Data, you will collaborate with teammates across Engineering, Product, and Customer Success to build impactful new features and iterate on existing ones to meet the evolving needs of our current and new customers. We are looking for data scientists who thrive in a collaborative, dynamic environment and will make a direct impact on the next version of our product as we scale to thousands of customers.

Responsibilities:

  • Run the existing MMM model for current and future customers, adapting the model to specific use-case requirements as required.
  • Play a critical role in advancing the MMM model’s capabilities by developing new and improved features
  • Develop advanced statistical models and machine learning algorithms
  • Collaborate with cross-functional teams to develop customized solutions that can become scalable product features
  • Communicate complex results to technical and non-technical stakeholders including internal teams and external customers
  • Effectively decompose complex sources, such as white papers, academic publications, and industry reports, repurposing them into custom solutions

Requirements:

  • Bachelor's or Master's degree in Computer Science, Engineering, Statistics, Mathematics, or related field, or equivalent experience
  • 2+ years of experience in statistical modeling, with a preference for experience in AdTech or marketing analytics
  • Expert in SQL and Python, including experience with standard data science libraries
  • Demonstrated experience in constructing and deploying statistical models
  • Solid understanding of predictive modeling and experience with libraries such as numpy and scipy
  • Exceptional problem-solving, critical thinking, and communication abilities

About Rockerbox:

Rockerbox started out as a marketing channel to help brands to attract new customers. However, we found that we were frequently given less credit due to misattributions that resulted from the fragmentation of marketing data across multiple platforms. So we pivoted the company to solve this problem. We created a platform where brands can integrate all of their data--addressing the data fragmentation problem--so they can understand the contribution of each marketing channel in bringing them new customers.

Rockerbox is in a continual state of evolution. We are not impeded by past decisions and strongly believe that what has gotten us to where we are may not be what gets us to where we want to be. We value transparency and encourage all Rockerboxers to speak their minds and take actions that will make the company or the product a better version of itself.

 

Benefits

  • Remote-first - work anywhere in the US
  • Health, vision, and dental insurance
  • Unlimited PTO
  • 10 Paid Holidays
  • Rockerbox Unplugged - we shut down the last week of the year
  • 12 weeks Parental Leave for all parents of a new child
  • Traditional and ROTH 401k options
  • $1000 annual training stipend

Rockerbox is a remote-first, equal opportunity employer and we actively encourage applicants from underrepresented backgrounds.

 

Please mention No Whiteboard if you apply!
I'm a one-man team looking to improve tech interviews, and could use any support! 😄


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