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Salary: USD 190,000 - 220,000 / annual
Metropolis is building AI systems to make the real world responsive, pioneering what they call the Recognition Economy. They're transforming parking into a seamless experience and expanding their intelligence layer across retail and hospitality.
In this role, you'll be the primary quantitative partner to Growth Marketing leadership, responsible for turning large-scale transactional and behavioral datasets into engines for user acquisition, retention, and profit expansion.
Key responsibilities:
- 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 the 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
Metropolis values in-person collaboration and operates an office-first model requiring employees to be on-site at least four days per week.
REQUIREMENTS:
- Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, Economics, or related quantitative field
- 6+ years of proven experience as a Data Scientist or Analytics Lead, with 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 (A/B testing, geo-testing, synthetic controls, media mix modeling)
- Strong business acumen with 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
PLUS (not required):
- Experience with cloud platforms (AWS S3, SageMaker, Airflow)
- Cloud data warehouses and modern data tools (Snowflake, dbt, Looker, Tableau)
- Media Mix Modeling (MMM) frameworks (Robyn, LightweightMMM)
- Machine learning frameworks (scikit-learn, TensorFlow, PyTorch) applied to user segmentation or churn prediction
- Ability to write clean, efficient, robust, well-commented code
