Staff Data Scientist

CSC Generation

8d

About The Position

CSC Generation is the AI-native holding company re-engineering omni-channel retail. We acquire iconic brands and transform them with Genesis—our operating platform unifying a Data Fabric, Automation Engine, proprietary tools, and shared services—to modernize operations, elevate customer experience, and expand margins.
With $1B+ in revenue across 13 brands, our portfolio includes Sur La Table, Backcountry, One Kings Lane, and more—premier home and outdoor banners that double as real-world innovation hubs. CSC Generation continues to grow through M&A, revitalizing companies with strong brand recognition and loyal customers.

We are looking for a Staff Data Scientist to lead the development of production-grade machine learning solutions that drive measurable business impact.

This is a senior individual contributor role requiring deep technical expertise, independent judgment, and the ability to influence cross-functional teams. You will own complex, ambiguous problems end-to-end, from problem framing through deployment and iteration.

Requirements

• MS in a quantitative field (Statistics, Computer Science, Operations Research or related discipline)
• 7+ years applied ML / data science experience
• Expert-level proficiency in Python / R, and SQL
• Familiarity with cloud data & ML platforms (GCP/Vertex AI, AWS/SageMaker)
• Proven track record of building production ML systems that delivered measurable business impact
• Deep understanding of model evaluation methodology, experimental design, and causal inference
• Ability to work with messy, incomplete, real-world data and make pragmatic tradeoffs
• Strong communication and influence skills
• Self-directed and autonomous

Nice To Haves

• Hands-on experience in e-commerce retail and pricing
• PhD in a quantitative field
• Track record of mentoring junior data scientists and leading technical projects

Responsibilities

• Lead the design and development of ML systems that solve complex, ambiguous business problems
• Make sound technical decisions on model architecture, evaluation methodology, and tradeoffs
• Set standards for model validation, testing, and monitoring across the team
• Identify when "good enough" is appropriate vs. when deeper investment is warranted
• Debug and troubleshoot models that fail in production - understand why they fail, not just that they fail
• Frame business problems as well-defined ML tasks with clear success criteria
• Build robust predictive models (classification, regression, time series, causal inference)
• Implement rigorous train/validation/test methodology to ensure real-world generalization
• Identify and prevent data leakage, overfitting, and other failure modes before they reach production
• Define metrics that align model performance with actual business outcomes
• Conduct holdout testing on true out-of-sample data - recognize when CV metrics are misleading
• Design and analyze experiments to measure causal impact
• Communicate model limitations, uncertainty, and risk to technical and non-technical stakeholders
• Partner with product, engineering, and business teams to ensure ML solutions solve real problems
• Translate complex technical concepts into actionable recommendations for stakeholders
• Contribute to hiring and technical interviews

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level