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.
Key Responsibilities – Technical Leadership:
- 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
Key Responsibilities – End-to-End Model Development & Evaluation:
- 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
Key Responsibilities – Influence & Collaboration:
- 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
Required Qualifications:
- 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
Preferred Qualifications:
- 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
What We're NOT Looking For
- Someone who only knows how to call .fit() and .predict() without understanding the underlying mechanics
- Someone who builds black-box models they can't explain, debug, or defend
- Someone who needs detailed instructions or hand-holding for ambiguous problems
- Someone who over-engineers solutions when a simple approach would suffice
