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Data Scientist - Testing

Data Scientist - Testing

Rockerbox empowers marketing executives to confidently make data-driven decisions, helping brands such as Tula, Figs, and Burton with the strategic decision-making that drives growth. To do so, Rockerbox offers a unique suite of product lines that centralize data and offer diversified measurement methodologies. The foundation of Rockerbox's solution is data centralization. Atop this foundation, the platform enables marketers to choose from a range of measurement methodologies, giving customers the flexibility to choose the most appropriate approach for their specific needs and questions.

As a Data Scientist at Rockerbox, you will take full ownership of testing methodologies and statistical experimentation, ensuring that marketers have precise insights into campaign effectiveness. This role involves developing and iterating on testing frameworks (planning through analysis), with a strong emphasis on adapting and refining the existing packages for our needs. If you enjoy designing rigorous experiments, optimizing statistical models, and working at the intersection of data science and product, this is the role for you.

Responsibilities

  • Own the end-to-end development of testing methodologies, ensuring statistical robustness.

  • Adapt, optimize, and iterate on the current testing packages, translating R-based methodologies into Python.

  • Design and implement statistical frameworks for campaign effectiveness measurement.

  • Work closely with engineering to integrate testing methodologies into the Rockerbox platform.

  • Conduct exploratory data analysis to refine test parameters and ensure accuracy.

  • Stay up to date with the latest causal inference and experimental design techniques.

Requirements

  • 5+ years of experience in data science.

  • Strong knowledge of statistical testing, experimental design, and regression modeling.

  • Proficiency in Python (NumPy, Pandas, Statsmodels, etc.); experience adapting R-based packages is a plus.

  • Familiarity with marketing analytics concepts such as campaign allocation and budget optimization.

  • Ability to translate complex statistical methodologies into actionable insights.

  • Strong problem-solving skills and experience working cross-functionally with engineering and product teams.

Why You’ll Love Rockerbox: At Rockerbox, you’ll find a fast-paced, results-driven environment where your work has a direct impact on our growth and the success of our clients. Our iterative development process means you’ll see your contributions come to life quickly. You’ll join a supportive, light-hearted team that values collaboration and innovation. We are committed to professional growth and will actively support your development in both technical and business domains.

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

  • Rockertreat! Annual company get-together

Rockerbox is a remote-first, equal opportunity employer. We actively encourage applicants from underrepresented backgrounds, and we are accepting candidates based anywhere in the United States.

Please note that we are not able to accommodate C2C candidates for this role. Rockerbox participates in E-Verify. As an employer utilizing E-Verify, we confirm the eligibility of all newly hired employees through an electronic database maintained by the U.S. Department of Homeland Security and the Social Security Administration.

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What You Should Know About Data Scientist - Testing, Rockerbox

Are you a Data Scientist passionate about testing methodologies? Join Rockerbox, where you'll make a real impact on how marketing executives make data-driven decisions. Working with brands like Tula, Figs, and Burton, your role will involve developing robust testing frameworks that empower marketers to understand their campaign effectiveness. At Rockerbox, you won't just analyze data; you'll own the entire process from planning through to analysis, ensuring that our clients get the most precise insights possible. Your proficiency in Python will be key, especially as you adapt and optimize existing testing packages from R. You'll collaborate closely with engineering teams to seamlessly integrate these methodologies into the Rockerbox platform, conducting exploratory data analysis to refine test parameters. Here, innovation and continuous professional development are encouraged, so you'll stay up to date with the latest in causal inference and experimental design techniques. In exchange for your expertise, Rockerbox offers a remote-first work environment with supportive colleagues, unlimited PTO, and extensive benefits. If you’re excited about designing experiments and optimizing statistical models in a dynamic setting, this Data Scientist - Testing position is your chance to thrive!

Frequently Asked Questions (FAQs) for Data Scientist - Testing Role at Rockerbox
What are the responsibilities of a Data Scientist - Testing at Rockerbox?

As a Data Scientist - Testing at Rockerbox, your key responsibilities include owning the development of testing methodologies that ensure statistical robustness, adapting and optimizing existing R-based packages into Python, and designing frameworks for measuring campaign effectiveness. You will conduct exploratory data analysis to refine test parameters and leverage your knowledge of causal inference to keep our strategies cutting-edge.

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What qualifications are required for the Data Scientist - Testing role at Rockerbox?

Rockerbox seeks candidates with at least 5 years of experience in data science, a strong grasp of statistical testing, and proficiency in Python, including libraries like NumPy and Pandas. Familiarity with marketing analytics concepts is ideal. Additionally, you should possess solid problem-solving skills and experience in cross-functional collaboration.

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Why is Python an important skill for the Data Scientist - Testing position at Rockerbox?

Python is crucial for the Data Scientist - Testing role at Rockerbox because you will be adapting and optimizing existing testing methodologies from R to Python. A strong command of Python will enable you to effectively implement statistical frameworks and perform complex data analysis, ensuring high-quality insights for our marketing clients.

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How does Rockerbox support professional growth for its Data Scientists?

At Rockerbox, professional growth is a core value. As a Data Scientist - Testing, you will benefit from a supportive work environment that encourages continuous learning and development. With initiatives like a $1000 annual training stipend and a commitment to innovative exploration, you can elevate both your technical and business skills.

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What type of work environment can a Data Scientist expect at Rockerbox?

A Data Scientist at Rockerbox can expect a remote-first work environment that prioritizes collaboration and support. The culture is fast-paced and results-driven, emphasizing teamwork and innovation, where your contributions directly impact the growth of both Rockerbox and its clients.

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Common Interview Questions for Data Scientist - Testing
Can you explain the process of designing a statistical experiment?

When discussing statistical experiment design, outline the steps: define your hypothesis, identify your variables, choose your sample size, and determine your measurement techniques. Emphasize the importance of ensuring robustness against biases and how your past experiences will help you tackle potential challenges.

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How do you translate complex statistical methodologies into actionable insights?

Illustrate your approach to simplifying complex statistics by using examples from past projects. Emphasize storytelling through data visualization and breaking down concepts into clear, relatable insights for stakeholders, especially in a marketing context.

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What techniques do you use for exploratory data analysis?

For exploratory data analysis, discuss the tools and methods you prefer, such as data visualization techniques, statistical summaries, and correlation analysis. Mention how these techniques help you identify trends, patterns, and anomalies that inform subsequent test designs.

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How do you ensure the statistical robustness of your testing methodologies?

Share your strategies for verifying statistical robustness, which may include using control groups, appropriate sample sizes, and sensitivity analyses. Describe your experiences or case studies where robust methodologies led to significant insights.

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What role does collaboration play in your data science projects?

Highlight the importance of collaboration with cross-functional teams, like engineering and marketing, ensuring that testing methodologies are practical and aligned with business goals. Share examples that demonstrate successful collaborative efforts.

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Can you walk us through a challenging project and how you approached it?

Choose a specific project at Rockerbox or similar settings, detailing the problem, your methodology, the obstacles faced, and how you streamlined the process. Highlight your analytical thinking, innovation, and resilience under pressure.

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Describe your experience with regression modeling.

Discuss your familiarity with regression analysis types, the software or programming languages you’ve used, and a project where regression modeling played a pivotal role in deriving insights for campaign optimization.

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What do you believe are the latest trends in statistical experimentation?

Mention current trends such as machine learning integration, A/B testing innovations, and advancements in causal inference techniques. Convey a passion for continuing education and staying updated with the latest research in the field.

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How do you handle unexpected results in data experiments?

Share your analytical approach to unexpected findings, emphasizing the importance of thorough investigation and validation. Highlight flexibility in adapting methodologies based on new insights while maintaining statistical integrity.

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What are your key performance indicators (KPIs) when measuring campaign effectiveness?

Identify critical KPIs relevant to campaign measurement, such as Return on Investment (ROI), conversion rates, and customer engagement metrics. Discuss how these metrics align with Rockerbox’s goals and influence decision-making.

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Full-time, remote
DATE POSTED
March 20, 2025

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