Decision Science Manager, Enterprise

We offer a hybrid work environment. Most US-based positions can also be performed remotely (any exceptions will be noted in the Minimum Qualifications below.)

Our Mission:

To actively connect people to their next great opportunity.

Who We Are:

ZipRecruiter is a leading online employment marketplace. Powered by AI-driven smart matching technology, the company actively connects millions of all-sized businesses and job seekers through innovative mobile, web, and email services, as well as through partnerships with the best job boards on the web. ZipRecruiter has the #1 rated job search app on iOS & Android.

Summary:

We are looking for a Manager, Decision Science to lead our Enterprise Decision Science team at ZipRecruiter. The Enterprise team covers the full lifecycle of how ZipRecruiter's largest customers find, attract, and hire talent. That runs from the performance marketing campaigns that drive traffic, to the apply experience and ATS integrations, to the signals that help employers identify the best candidates.

As a Manager of the Enterprise Decision Science team, you will be a true player-coach: developing a small team of decision scientists while personally leaning in on strategic analyses in your problem space.

You can bring rigor to evaluation in a space where clean experiments are rare and the data is sometimes too thin for formal causal methods. You know when to reach for a quasi-experimental design and when to build a well-reasoned recommendation from directional data and qualitative signals and communicate recommendations clearly to Product, Engineering, and GTM partners.

Key Focuses:

Lead & Develop the Team:

Manage and coach a team of decision scientists supporting the Enterprise area. Review and coach on their work, own performance and career development conversations, set clear expectations, and help each person build on their strengths and grow their business and technical skills. Prioritize across multiple concurrent projects, align the team's capacity with the goals of the Enterprise area and the company, and find ways to make the team more efficient, including setting standards for Data and AI tooling.

Player-Coach Technical Leadership:

Personally lead strategic analyses in the Enterprise space and be comfortable diving into the details. Set methodological standards for the team, pressure-test causal claims, and coach the team on the right level of rigor for each question. That includes knowing when "good enough to decide" beats "statistically conclusive."

Lead Decision Science for the Enterprise Area:

Own the decision science agenda for Enterprise. Partner with stakeholders to define which questions are worth asking, set analytical priorities, and hold the team to a high bar of rigor. The team covers the full Enterprise lifecycle, from campaign-driven traffic through the apply experience, ATS integrations, and candidate-quality signals.

Measure GTM & Sales-Led Initiatives:

Evaluate the success of initiatives led by Sales, GTM, and Customer Success. With a smaller base of large customers, randomized tests are often not an option, and sometimes there aren't enough accounts or events for causal inference methods either. You will pick the strongest approach the data can support. That may be a quasi-experimental design, a descriptive before/after read, or a combination of quantitative results with qualitative signals such as customer feedback, Customer Success and Sales input, and account-level context.

You will be clear with partners about how confident each conclusion is.

Cross-Functional Partnership & Influence:

Serve as the trusted Decision Science partner for Enterprise Product, Engineering, Rev Ops, Sales/GTM leadership, and Customer Success. Influence direction and prioritization within the cross-functional team and find new opportunities to partner. Turn complex quantitative findings into clear, actionable recommendations for technical and non-technical audiences, including senior stakeholders.

Minimum Qualifications:

• Bachelor's degree in a quantitative field such as mathematics, statistics, computer science, operations research, economics, or equivalent experience.
• 6+ years of analytics or data science experience in a product/engineering/tech environment.
• 1+ years of people management experience, including direct responsibility for performance management and career development.
• Demonstrated ability to lead data work on important business initiatives while earning the trust of cross-functional and business stakeholders.
• Experience measuring impact with both A/B tests and quasi-experimental or causal inference methods (e.g., difference-in-differences, synthetic control, matching) when randomization isn't feasible.
• Sound judgment in low-data situations. You can combine limited quantitative evidence with qualitative signals to reach a well-reasoned recommendation, and you communicate the uncertainty honestly.
• Strong business acumen and a demonstrated ability to connect data analysis to measurable business outcomes.
• Excellent communication and presentation skills, with a track record of synthesizing complex findings for both technical and non-technical audiences.

Preferred Qualifications:

• Experience with performance marketing campaign measurement and optimization
• Experience analyzing customer retention, churn, and expansion in a B2B or enterprise SaaS business.
• Experience partnering with Sales, GTM, Rev Ops, or Customer Success teams to evaluate business initiatives.
• Experience working with small-sample or account-level data, such as B2B customer bases where each customer has an outsized impact.
• Familiarity with recruiting, HR tech, or ATS ecosystems, or with two-sided marketplaces.
• As part of our team you’ll enjoy: Competitive salary
• Exceptional benefits package
• Flexible Vacation & Paid Time Off
• Employer-matched 401(k) plan

The US base salary range for this full-time position is $177,500-$235,000 Our salary ranges are determined by role, level, and location, and the range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location, role-related knowledge and skills, depth of experience, relevant education or training, and additional role-related considerations.

Depending on the position offered, equity, bonuses, commission, or other forms of compensation may also be provided as part of a total compensation package, in addition to a full range of medical, financial, and other benefits.

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