Staff Data Scientist

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