Senior Data Scientist - Marketing
New
O
OuraDigital Health, E-commerce
Remote - United StatesFull-TimeSenior
SalaryRegion 1 $172,550 - $203,000; Region 2 $158,950 - $187,000; Region 3 $147,900 - $174,000
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Job Details
- Experience
- 6+ years
- Required Skills
- AWSPythonSQLCloud ComputingMachine LearningSnowflakeData sciencedbtDatabricks
Requirements
- 6+ years of experience in data science, machine learning, or advanced analytics.
- Domain experience supporting Marketing, Growth, or Retail functions in digital businesses.
- Proven track record of owning high-impact business problems end to end.
- Hands-on experience building marketing data science solutions such as spend optimization or media mix models.
- Demonstrated ability to build decision systems that operationalize analytical outputs into business workflows.
- Experience with data engineering, including transforming and unifying data across multiple systems.
- Proficiency in experimentation, causal inference, forecasting, and optimization methods.
- Strong technical proficiency in SQL (dbt) and Python.
- Experience applying AI techniques such as LLM-enabled workflows or recommendation systems.
- Experience with modern cloud data platforms like Databricks, AWS, or Snowflake.
- Ability to communicate complex analytical concepts to non-technical partners.
- Proven experience partnering with cross-functional and distributed teams.
Responsibilities
- Develop models and optimization approaches for spend allocation, channel mix, and revenue forecasting.
- Design and operationalize decision systems such as recommendation engines and scenario planning tools.
- Lead experimentation and causal measurement across marketing programs and retail partnerships.
- Build and maintain data foundations that unify marketing, revenue, and product data.
- Create dashboards and analytical products to provide visibility into funnel health and performance.
- Partner with stakeholders to translate business questions into analytical frameworks and actionable recommendations.
- Collaborate with Data Engineering to improve data quality, instrumentation, and pipeline reliability.
- Apply AI/ML methods including predictive modeling and LLM-enabled workflows to improve decision-making.
- Translate analytical findings into clear insights for executive stakeholders.
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