[Hiring] Senior Quantitative Analyst @Schumacher

Role Description

We're looking for a hands-on, Senior Quantitative Analyst, Product Portfolio (Statistical Modeling) to own analytics and reporting in support of our Portfolio Development team, with a specific focus on SKU productivity & capital allocation across a portfolio of 20,000+ SKUs and growing. This role will report directly to the Sr. Director of AI & Data Insights, but is expected to work directly with the CEO & Chief of Staff on a day-to-day basis.

This is not a role for someone who takes data at face value. You'll need to distinguish real signal from noise (e.g., a SKU selling poorly because it's out of stock, not because demand is weak), and when you find data that's wrong or inconsistent, you'll have the access and judgment to go into Snowflake and fix it — not just flag it and wait.

What You’ll Do

• Work hands-on with CEO & Product Strategy stakeholders to understand key strategic questions, and then build analysis and reporting to address them directly.
• Use statistical methods (e.g. conjoint analysis, multivariate regression) to identify which product attributes and characteristics (e.g., color, pattern, material, price point, availability) drive productivity, and translate that into data-driven guidance on where the portfolio should expand, contract or shift.
• Cut through data noise at scale: systematically identify and control for confounders like stock-outs, new/discontinued SKUs, and seasonality so reported trends reflect real pattern performance.
• Identify data inconsistencies at the source (e.g., ERP, web, product master data) and directly and systematically correct records in Snowflake and work with upstream platform owners to improve the accuracy of source data and downstream analytics.
• Present findings clearly and independently to executive audiences, adapting the message for technical or non-technical stakeholders.
• Recommend and maintain recurring analytics & reporting to provide accurate and timely snapshot of SKU productivity.
• Use AI across your own workflow to explore data faster, automate repetitive reporting, catch errors, and multiply output given the scale and growth of the SKU portfolio.

Qualifications

• 2-5 years in data science, analytics, statistics in similar SKU-driven businesses.
• Solid grounding in statistics or operations research (e.g. regression, hypothesis testing, multivariate analysis, optimization).
• Able to move beyond descriptive reporting to model relationships between variables and quantify drivers, not just flag correlations.
• Strong SQL and Python, with hands-on Snowflake experience including writing back to correct or clean production data (not just read-only querying).
• A track record of presenting findings and challenging assumptions directly with senior stakeholders and executives.
• Independent executor — sets analysis priorities against business goals without waiting for direction, and ships accurate, high-impact work in a timely manner.
• Curiosity — asks why the numbers look the way they do and keeps digging until the answer holds up, rather than reporting the first number that looks plausible.
• Thinks in terms of SKU-level economics: sell-through, margin, inventory position, lifecycle stage — and knows how to separate a true productivity problem from a data artifact.
• Treats data quality as part of the job, digging into root causes and correcting them directly rather than working around bad data.
• Hands-on experience applying AI/ML in a business context for task automation - automating repetitive analysis to spend more time on judgment calls.
• Strong English communication skills and a high ownership mindset.

Requirements

• Experience with Power BI or similar BI tools alongside Snowflake.
• Exposure to margin, pricing, or demand forecasting data.
• Experience in retail, e-commerce, or luxury/home furnishings goods.

Benefits

• Competitive salary and benefits, based on experience.
• Fully remote work within Europe, with optional time in our NYC office (1–2 weeks per year).
• Direct visibility with the CEO, with real influence on Product Strategy and Merchandising decisions.
• Collaborative, inclusive, and low-ego working environment.