Sr Data Scientist - Hybrid

About the Role

Key Responsibilities

• Build and maintain advanced forecasting models (e.g., time series, machine learning, causal models) to support product planning and strategic decision-making across business units.
• Incorporate a wide range of internal and external data sources (e.g., sales, macro trends, market data, pricing, promotions) to improve forecast accuracy and business insight.
• Utilize Large Language Models (LLMs) and AI technologies to enhance forecasting approaches, uncover new insights, and automate key forecasting workflows where appropriate.
• Develop and execute scenario planning models to provide a range of forecast outcomes based on varying market and business assumptions.
• Work closely with partners across Supply Chain, Product, Finance, Commercial, and Marketing to align forecasts with business needs.
• Translate model results into clear, actionable recommendations tailored to both technical and executive audiences.
• Identify key business drivers and continuously refine models to reflect changing market dynamics and product strategies.
• Contribute to the development of scalable forecasting tools and infrastructure, enabling faster insights and decision-making.

Requirements

• 3-5 years of experience in data science, with a strong focus on time series forecasting, predictive modeling, or demand planning.
• Experience forecasting in luxury goods, premium retail, lifestyle brands, or similar industries is strongly preferred.
• Hands-on experience working with AI technologies, including the application of Large Language Models (LLMs) and generative AI to forecasting, data enrichment, or automation tasks.
• Proficiency in Python or R, with demonstrated experience using forecasting libraries (e.g., Prophet, ARIMA, XGBoost, or similar).
• Strong SQL skills and experience working with large-scale datasets in a cloud environment (AWS, GCP, or Azure).
• Demonstrated experience in scenario planning for forecasts, including the creation of multiple forecast scenarios based on varying assumptions using internal and external variables.
• Deep understanding of statistical methods and machine learning techniques relevant to forecasting and causal inference.
• Excellent communication skills with a proven ability to translate analytics into business value.
• Bachelor's degree in Statistics, Data Science, Computer Science, Economics, or a related field.

Nice to Have

• Advanced degree in Statistics, Data Science, Computer Science, Economics, or a related field.
• Experience with demand forecasting in multi-channel environments (e.g., DTC, retail, global distribution).
• Familiarity with modern data tools and platforms (e.g., dbt, Snowflake, Airflow).
• Experience developing or fine-tuning Large Language Models (LLMs), and familiarity with tools like LangChain, Hugging Face, or OpenAI APIs.
• Experience with dashboarding and visualization tools like Tableau or Power BI.

Qualifications

• Bachelor's degree in Statistics, Data Science, Computer Science, Economics, or a related field.

Benefits & Perks

• Inclusive compensation package including annual bonus programs.
• Health insurance benefits.
• 401k program.
• Onsite fitness centers and employee stores.
• Employee discounts on products and accessories.