Staff Data Scientist, Marketing
New
Based in the United StatesFull-TimeStaff
SalaryCompetitive base salary aligned with U.S. market benchmarks and experience level
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonSQLMachine Learning
Requirements
- 5+ years of hands-on experience in applied data science or machine learning roles with measurable business impact, ideally in digital marketing, adtech, or performance marketing environments.
- Strong experience building and deploying ML models in production, including within AWS or similar cloud environments.
- Advanced proficiency in Python and SQL, with the ability to work directly with large-scale datasets.
- Deep understanding of ML techniques such as ranking systems, recommendation engines, reinforcement learning, and predictive modeling (e.g., LTV, attribution, conversion modeling).
- Strong business acumen with the ability to translate marketing and revenue goals into modeling frameworks and data-driven solutions.
- Experience working with cross-functional stakeholders in fast-paced, data-driven environments.
- Excellent communication skills with the ability to explain complex technical concepts to non-technical audiences.
- Strong ownership mindset with the ability to operate end-to-end from problem definition to production impact.
Responsibilities
- Own end-to-end development of machine learning models that optimize marketing performance, from problem framing and feature engineering to deployment support and post-launch performance monitoring.
- Build and iterate on models for user acquisition, bidding efficiency, lead quality, funnel optimization, and lifetime value prediction, with a strong focus on ROAS impact.
- Design and implement advanced modeling approaches such as reinforcement learning, multi-armed bandits, and recommendation/ranking systems to improve targeting and conversion efficiency.
- Analyze large-scale marketing and user behavior datasets to identify optimization opportunities across campaigns, funnels, creatives, and acquisition channels.
- Partner with ML engineering teams to ensure smooth production deployment, scalability, and ongoing model performance monitoring.
- Collaborate with marketing, product, and business stakeholders to translate business objectives into measurable data science solutions and experimentation frameworks.
- Continuously identify new opportunities to improve monetization, efficiency, and user acquisition through data-driven insights and modeling innovation.
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