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Senior Manager, Data Science - Rockerbox

Remote, USA

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.

 

About the Role:

As Senior Manager, Data Science at Rockerbox, you will lead and scale our data science team while also contributing as a hands-on practitioner. This role is a player-coach position, balancing leadership responsibilities with direct execution in feature development for testing, MMM, and multi-touch attribution (MTA). You will drive the strategic evolution of our data science initiatives, mentor a team of talented data scientists, and ensure that our methodologies remain cutting-edge. If you thrive in a fast-paced environment where you can both manage and build, this role is for you.

 

Responsibilities:

  • Lead and mentor our lean data science team, fostering growth and technical excellence
  • Own feature development for testing, MMM, and MTA models, ensuring methodological rigor and scalability
  • Act as a hands-on contributor, developing statistical models and validating approaches for both testing and attribution
  • Work cross-functionally with engineering and product teams to integrate data science solutions into Rockerbox’s platform
  • Drive best practices in model validation, experiment design, and data pipeline efficiency
  • Identify opportunities to leverage AI and machine learning for enhanced insights and automation
  • Collaborate with leadership to define team growth, hiring needs, and strategic priorities

 

Requirements:

  • 7+ years of experience in data science, analytics, or machine learning, with at least 1-2 years in a leadership role.
  • Strong proficiency in Python, SQL, and cloud-based data platforms.
  • Deep understanding of statistical modeling, causal inference, and regression techniques.
  • Hands on experience working with Bayesian modeling.
  • Someone with AI vision
  • Experience in feature development for MTA models and testing methodologies; experience with marketing data and consumer business KPIs.
  • Ability to hire, manage, and mentor data scientists while contributing as an IC.
  • Strong communication skills to effectively collaborate with engineers, product teams, and stakeholders.

 

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.

 

The successful candidate’s starting salary will be determined based on a number of non-discriminating factors, including qualifications for the role, level, skills, experience, location, and balancing internal equity relative to peers at DV.
The estimated salary range for this role based on the qualifications set forth in the job description is between [$128,000 - $230,000]. This role will also be eligible for bonus/commission (as applicable), equity, and benefits.
The range above is for the expectations as laid out in the job description; however, we are often open to a wide variety of profiles, and recognise that the person we hire may be more or less experienced than this job description as posted.

Not-so-fun fact: Research shows that while men apply to jobs when they meet an average of 60% of job criteria, women and other marginalized groups tend to only apply when they check every box. So if you think you have what it takes but you’re not sure that you check every box, apply anyway!

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