Who we are

The real world is the next frontier, and at Metropolis, we are creating the artificial intelligence to make it responsive. We are pioneering the Recognition Economy — a future where mundane repetition disappears and being known unlocks access, comfort and belonging everywhere you go. From transforming parking into a seamless drive-in, drive-out experience for millions of Members to expanding our intelligence layer across retail and hospitality, we are building a world that feels instinctive and magical. The future isn’t coming; it’s here, and we need builders, innovators and problem solvers to help us create it.

Who you are

Metropolis is seeking a Senior Data Scientist, Growth Marketing to join our growing data team. In this role, you will serve as the primary quantitative partner to our Growth Marketing leadership, turning massive real-world transactional and behavioral datasets into engines for user acquisition, retention, and gross profit expansion. You bring a deep background in marketing measurement and causal inference, with proven experience building multi-touch attribution systems, modeling customer lifetime value (LTV), and designing rigorous marketing experiments. You excel not only at building complex predictive models, but also at translating high-level data insights into clear business strategies for cross-functional stakeholders across Growth, Product, and Finance.

What you'll do

Design, deploy, and refine predictive models for Multi-Touch Attribution (MTA), Customer Lifetime Value (CLV), and Customer Acquisition Cost (CAC) efficiency to guide multi-channel marketing budget allocation
Conduct deep-dive cohort analyses and retention modeling to uncover drivers of user conversion, repeat behavior, and churn across our location-based growth funnel
Lead the growth experimentation roadmap by designing, executing, and analyzing A/B tests, geo-experiments, holdout tests, and incrementality studies across digital and physical acquisition channels
Partner directly with Growth Marketing and Finance leaders to optimize unit economics, justify marketing spend against gross profit targets, and set channel-level growth goals
Collaborate with analytics and data engineering teams to establish event tracking requirements, build scalable growth data pipelines, and develop automated executive dashboards
Utilize advanced AI tools and modern analytics frameworks to transform static analytical workflows into responsive, high-output processes

What we're looking for

Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, Economics, or a related quantitative field
6+ years of proven experience as a Data Scientist or Analytics Lead, with a distinct focus on growth marketing, performance media, or adtech
Advanced proficiency in Python and SQL for complex data manipulation, statistical modeling, and predictive analytics
Deep domain expertise in growth measurement frameworks, multi-touch attribution, cohort retention modeling, and LTV/CAC dynamics
Hands-on experience with statistical experimentation and causal inference techniques such as A/B testing, geo-testing, synthetic controls, and media mix modeling
Strong business acumen with a track record of partnering with senior executives in Growth, Product, and Finance to drive measurable bottom-line growth
Excellent analytical, problem-solving, and communication skills to make complex data stories intuitive and actionable
Experience leveraging AI tools to transform static workflows into responsive, high-output processes

While not required, these are a plus

Experience with cloud platforms, including AWS S3, SageMaker, and Airflow
Experience with cloud data warehouses and modern data tools such as Snowflake, dbt, Looker, and Tableau
Familiarity with Media Mix Modeling (MMM) frameworks including Robyn and LightweightMMM
Hands-on experience with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch applied to user segmentation or churn prediction
Ability to write clean, efficient, robust, and well-commented code to produce high-quality data science procedures