Marketing Data Science Manager

KAYAK•Cambridge, MA

6d•$160,000 - $200,000

About The Position

KAYAK is seeking a Data Science Manager to lead its Marketing Data Science team. The team develops models, experimentation frameworks, and measurement systems that guide global marketing investment and reveal performance across SEM, SEO, brand, and the wider marketing organization. The manager will set technical direction for marketing measurement, experimentation, and advanced analytics, remain hands-on in key projects, build and develop a trusted team, and partner with marketing, technology, and business colleagues to turn analysis into decisions that drive efficient growth. The team covers marketing measurement—including attribution, marketing mix modeling, incrementality and geo-lift testing, value estimation, and user lifetime value—as well as the end-to-end marketing experimentation lifecycle. Its work also includes brand media measurement; user journey and landing page analytics; SEO measurement; and paid search analytics for performance diagnostics, bidding, budget allocation, and forecasting. The role leads and develops data scientists, sets the team roadmap and priorities, owns standards and processes for experimentation, and oversees the development of integrated marketing measurement frameworks. It translates analytics into recommendations on budget allocation, channel strategy, and test priorities, and works with data engineering, machine learning, and software engineering teams to build reusable data products. The manager will contribute directly to modeling, analysis, and code review while progressively dedicating more time to leadership, and will keep the team informed about changes in data privacy, marketing platform measurement, and AI-driven search and advertising. KAYAK is part of Booking Holdings and operates global metasearch brands including Momondo, Cheapflights, and HotelsCombined. The role is Massachusetts-based. The stated salary range is $160,000–$200,000, excluding annual bonus and recurring RSU grants.

Requirements

Experience leading or mentoring data scientists or analysts in a team setting, with a track record of helping people grow and do their best work.
Hands-on experience with statistical inference, experimentation, and causal inference, including A/B testing, geo experiments, and incrementality measurement.
Experience with marketing measurement methods such as marketing mix modeling, attribution, and LTV modeling.
Experience in analytics for at least one of paid search/PPC, SEO, or brand/upper-funnel media, and curiosity to build depth in the other areas.
Strong Python and SQL skills.
Experience building and deploying models in a production or near-production environment.
Ability to communicate technical work clearly to marketing, product, and business partners without a data science background.
Collaborative, non-hierarchical, and humble leadership style.

Nice To Haves

Experience with econometric modeling, Bayesian methods, or causal modeling.
Familiarity with web analytics and user journey or funnel analysis.
Experience in online travel, e-commerce, or another performance-marketing-driven business.
Familiarity with open-source marketing mix modeling tools such as Meridian, Robyn, or PyMC-Marketing.
Experience measuring traffic from AI search and LLM-driven channels.

Responsibilities

Lead, coach, and develop a team of data scientists, setting goals, supporting career growth, and fostering curiosity and ownership.
Set the team's roadmap and priorities in line with company goals, focusing on high-impact work across SEM, SEO, brand, and other channels.
Own the marketing experimentation process and establish standards for test design, statistical rigor, and decision-making.
Lead development of measurement frameworks combining marketing mix modeling, attribution, and incrementality testing to assess channel and brand effectiveness.
Oversee analytics for paid search, SEO, brand media, and user journey and landing page performance, translating findings into recommendations for budget allocation, channel strategy, and test priorities.
Partner with data engineering, machine learning, and software engineering teams to build scalable, reusable data products.
Remain hands-on in priority technical work, including modeling, analysis, and code review, while gradually shifting time toward team leadership.
Keep the team current on changes in data privacy, marketing platform measurement, and AI-driven search and advertising.

Benefits

Work from (almost) anywhere for up to 20 days per year.
Company-paid therapy sessions through SpringHealth.
Company-paid HeadSpace subscription.
Company-wide week off each year.
No-meeting Fridays.
Paid parental leave.
Generous paid vacation and time off for birthdays.
Paid volunteer time.
Development Dollars.
Leadership development.
Access to thousands of on-demand e-learnings.
Travel discounts.
Employee Resource Groups.
Competitive retirement and health plans.
Free lunch two days per week.
Quarterly social events.
Health benefits.
Flexible spending account.
Retirement benefits.
Life insurance.
Paid time off, including PTO, paid sick leave, medical leave, bereavement leave, floating holidays, and paid holidays.
Parental leave benefits.
Annual bonus and recurring RSU grants.