• Partner with marketing, sales, and operations teams to define business problems, establish success metrics, and identify opportunities to improve conversion, revenue, and efficiency
• Explore and combine data from multiple sources using SQL and Python. Investigate data quality issues, develop features, and document assumptions
• Build and evaluate predictive models for use cases such as customer propensity, lifetime value, segmentation, and forecasting
• Apply statistical analysis and experimentation to understand performance drivers, measure impact, and communicate uncertainty and limitations
• Develop reusable analytical workflows and automate recurring analyses to support faster, more consistent decision-making
• Collaborate with engineering teams to integrate models into business workflows and monitor their performance over time
• Apply AI and LLMs to use cases such as extracting insights from unstructured data and supporting analytical workflows. Evaluate output quality, reliability, cost, and appropriate handling of data
• Present findings to technical and nontechnical audiences, explain trade-offs, and recommend clear next steps

Benefits

• Semi-Annual Bonuses
• Family Planning Benefits
• $500 WFH Stipend
• Flexible PTO
• Company 401k Match
• Fitness Reimbursement
• Learning & Development
• Mental Health Support
• Health, Vision, and Dental Benefits

• Strong communication skills and the ability to collaborate across technical and business teams
• Experience with GCP, BigQuery, or comparable cloud data platforms
• Experience with forecasting, propensity modeling, customer lifetime value, or experiment design
• Familiarity with LLM APIs, embeddings, retrieval-augmented generation (RAG), or evaluating AI-powered applications
• Strong SQL skills and proficiency in Python for data preparation, analysis, modeling, and evaluation
• Experience in digital marketing, customer acquisition, sales, or customer analytics
• A solid foundation in statistics and machine learning, including regression, classification, clustering, and hypothesis testing
• Ability to translate ambiguous business questions into a structured analytical approach and actionable recommendations
• Experience with feature engineering, model validation, and selecting appropriate evaluation metrics, including an understanding of overfitting and data leakage
• 2+ years of experience in data science or a closely related role applying statistical analysis and machine learning to business problems
• Attention to data quality, reproducibility, and documentation, with familiarity using version control
• Ability to work with AI and LLMs, demonstrated through professional, academic, or personal projects. Familiarity with prompting, working with model outputs, and evaluating their accuracy and reliability
• Experience partnering with engineering teams to deploy and monitor models