Job Description

A well-known apparel brand is seeking a Senior Data Scientist , to be responsible for turning complex data into actionable strategies across the organization. You will master the full analytics lifecycle: from designing executive-ready data visualizations to build/productionize robust Machine Learning (ML) models. If you have a passion for the e-commerce space, this is a phenomenal opportunity to drive tangible, data-driven results on a fast-growing business.

Location: Beverly Hills, CA (4 days/week)

Salary: Up to 200k total

Responsibilities:

• Independently execute complex, deep-dive exploratory data analysis to proactively identify non-obvious, actionable insights and significant trends that directly influence business strategy and investment.
• Design, build, and maintain a standardized suite of interactive dashboards and executive-level reports, utilizing advanced visualization techniques to effectively communicate data narratives to senior stakeholders.
• Own the end-to-end development, deployment, and performance monitoring of ML models designed to solve critical business challenges (e.g., forecasting, recommendation engines).
• Drive innovation by proactively researching, experimenting with, and building proofs-of-concept for how AI can solve business problems.
• Scope, conduct, and deliver comprehensive ad-hoc analysis for the most ambiguous business questions, providing clear, well-supported conclusions and strategic recommendations.
• Continuously evaluate and optimize existing data processes, tooling, and methodologies to enhance the speed and quality of analytical output.

Requirements:

• 3+ years of experience in a Data Analyst or similar quantitative role.
• Bachelor's or Master's degree in a quantitative field (e.g., Applied Statistics, Mathematics, Data Science, Computer Science, Economics).
• Expert-level analytical, critical thinking, and structured problem-solving skills, with a proven ability to handle ambiguous and unstructured problems.
• Advanced proficiency in SQL, including deep experience writing highly complex queries, tuning query performance, and implementing robust data quality checks.
• Strong programming skills in Python with practical experience utilizing libraries for data manipulation (e.g., Pandas), statistics, and ML (e.g., Scikit-learn, TensorFlow, PyTorch).
• Proven expertise with data visualization tools such as Looker, Tableau, or PowerBI
• Experience with Snowflake (AWS, or GCP)
• Deep understanding and practical application of statistical concepts (e.g., hypothesis testing, regression analysis, experimental design, A/B testing).
• Proven experience with the full Machine Learning lifecycle, including feature engineering, model selection, hyperparameter tuning, cross-validation, and production deployment

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