Rippling is the first way for businesses to manage all of their HR & IT—payroll, benefits, computers, apps, and more—in one unified workforce platform.

By connecting every business system to one source of truth for employee data, businesses can automate all of the manual work they normally need to do to make employee changes. Take onboarding, for example. With Rippling, you can just click a button and set up a new employees’ payroll, health insurance, work computer, and third-party apps—like Slack, Zoom, and Office 365—all within 90 seconds.

Based in San Francisco, CA, Rippling has raised $1.2B from the world's top investors—including Kleiner Perkins, Founders Fund, Sequoia, Bedrock, and Greenoaks—and was named one of America's best startup employers by Forbes (#12 out of 500)

About the role

As the Senior Data Scientist for Marketing at Rippling, you will join a team of data scientists and analysts dedicated to boosting the impact of all Rippling’s top of funnel motions - growth marketing, product marketing, brand, and cross-sell

Given how marketing is critical for our business, as a senior player, your work will not only elevate the technical caliber of our marketing teams but also fuel a powerful growth engine for all Rippling.

What you will do

Own and the data science and analytics roadmap of key marketing motions - e.g. growth marketing, product marketing, brand marketing, and cross-sell marketing
Influence, work collaboratively and deeply embedded in sophisticated teams of marketing practitioners, sales, engineers, finance, and product managers
Define, measure, and provide insights on how to accelerate success across all marketing teams (e.g. marketing operations, growth engineering, marketing practitioners)
Establish robust data assets and pipelines to collect, clean, and transform marketing data, ensuring integrity, accessibility for analysis and modeling, and scalability for new business lines, channels, and geographies
Design, improve, and maintain business intelligence for all our marketing teams
Develop and launch machine learning models that drive business impact (e.g. forecast customer behavior, identify highest-value prospects, personalize marketing messages)
Design and implement measurement and attribution frameworks across marketing channels and funnel motions (e.g. between sales teams and marketing campaigns)
Present data-driven insights and recommendations to influence executive decisions
Make a positive difference to our growth trajectory

What you will need

Track record of applying data science and analytics to solve marketing challenges
Knowledge of marketing concepts, including customer acquisition strategies, marketing channels, and attribution modeling
Proficiency with techniques and algorithms for experimentation, modeling, recommendation and decision systems
Fluency in SQL and Python
Familiarity with business intelligence best practices and tooling
Familiarity with data transformation best practices and tooling (e.g. incremental tables, metrics layer, dbt projects)
Exceptional communication, capable of collaborating effectively with both technical and non-technical audiences
Passion for nurturing a culture of data-driven decision-making and long-term impact within a fast-paced, ambiguous, hyper-growth environment

Additional Information

Rippling highly values having employees working in-office to foster a collaborative work environment and company culture. For office-based employees (employees who live within a 40 mile radius of a Rippling office), Rippling considers working in the office, at least three days a week under current policy, to be an essential function of the employee's role.

This role will receive a competitive salary + benefits + equity. The salary for US-based employees will be aligned with one of the ranges below based on location; see which tier applies to your location here.

A variety of factors are considered when determining someone’s compensation–including a candidate’s professional background, experience, and location. Final offer amounts may vary from the amounts listed below.

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