Responsibilities
- Lead full lifecycle development of models and features as part of broad marketing technology projects, from initial data investigation to live deployment
- Assist in shaping success criteria and analytical strategies, seeking guidance from senior members when problem definitions are unclear
- Maintain high analytical standards while respecting business limitations and project deadlines
- Convert marketing challenges into structured, model-based investigations
- Work effectively in settings where formal experimentation is not possible, using proven techniques from marketing mix modeling, experimental design, and observational studies
- Clearly state and record assumptions made during analysis and explain how they influence business decisions
- Deliver meaningful insights even with incomplete or uncertain data, clearly outlining constraints and compromises
- Recognize and escalate inconsistencies across different measurement methods, such as attribution, incrementality, and MMM, for review with experienced team members
- Ensure analytical outputs support practical business actions and consider real-world limits like budget, timing, and channel interdependencies
- Help build and use analytical frameworks covering marketing mix modeling, incrementality testing (including geo-based experiments and synthetic controls), bidding systems, or customer lifetime value estimation
- Implement validation techniques when true outcomes are unknown, using approaches like method comparison, historical simulation, or sensitivity checks
- Adhere to team-wide standards for statistical accuracy, model clarity, and reproducible results
- Engage in sharing knowledge and best practices within the data science community and immediate team
- Turn complex model results into clear, compelling stories for non-technical audiences
- Communicate effectively with team stakeholders, adjusting technical depth based on audience needs
- Respond to inquiries about model results with transparency and clear context, including known limitations
- Build robust, scalable solutions following strong engineering practices in testing, monitoring, documentation, and reproducibility
- Collaborate closely with engineering and product teams to implement and refine data systems
- Design models to be sustainable decision-making tools, not one-time analyses
Work Arrangement
Remote (Worldwide)
