Senior Data Scientist, Growth Marketing

Metropolis•Chicago, IL

1d•$170,000 - $200,000•Onsite

About The Position

Metropolis is seeking a Senior Data Scientist, Growth Marketing, to serve as the primary quantitative partner to Growth Marketing leadership. The role turns large real-world transactional and behavioral datasets into engines for user acquisition, retention, and gross profit expansion. The successful candidate brings expertise in marketing measurement and causal inference, including multi-touch attribution, customer lifetime value modeling, and rigorous marketing experimentation. They will build predictive models and translate analytical insights into clear business strategies for stakeholders across Growth, Product, and Finance. The position designs and refines models for multi-touch attribution, customer lifetime value, and customer acquisition cost efficiency; analyzes cohorts and retention across a location-based growth funnel; and leads experimentation across digital and physical acquisition channels. It also partners with Growth Marketing and Finance on unit economics and marketing spend, collaborates with analytics and data engineering teams on tracking, data pipelines, and dashboards, and uses AI tools and modern analytics frameworks to improve analytical workflows. Metropolis uses an office-first model requiring corporate team members to work onsite at least four days per week. The anticipated base salary is $170,000–$200,000 USD annually. Compensation may also include healthcare benefits, a 401(k), disability coverage, basic life insurance, stock options, bonus plans, and other components.

Requirements

Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, Economics, or a related quantitative field.
At least 6 years of experience as a Data Scientist or Analytics Lead, with a focus on growth marketing, performance media, or adtech.
Advanced proficiency in Python and SQL for complex data manipulation, statistical modeling, and predictive analytics.
Deep 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 and a record of partnering with senior executives in Growth, Product, and Finance to drive measurable bottom-line growth.
Excellent analytical, problem-solving, and communication skills, including the ability to make complex data stories intuitive and actionable.
Experience using AI tools to transform static workflows into responsive, high-output processes.

Nice To Haves

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.

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 cohort analyses and retention modeling to identify drivers of user conversion, repeat behavior, and churn across the location-based growth funnel.
Lead the growth experimentation roadmap, designing, executing, and analyzing A/B tests, geo-experiments, holdout tests, and incrementality studies across digital and physical acquisition channels.
Partner 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.
Use advanced AI tools and modern analytics frameworks to transform static analytical workflows into responsive, high-output processes.