The Role
Lead the development of production-grade machine learning solutions, collaborating with cross-functional teams to solve complex business problems and deliver measurable impact.
Summary Generated by Built In
CSC Generation operates $1B+ in revenue across 13 retail brands including Sur La Table, Backcountry, One Kings Lane and more. Pricing is central to how we compete, and we're building the kind of ML-driven pricing capability that the best e-commerce companies in the world have invested in for years.
We're looking for a Staff Data Scientist who's an expert in building and shipping production pricing systems — demand forecasting, price elasticity modeling, dynamic pricing, and the experimentation infrastructure that tells you whether any of it is actually working.
It's a hard problem with real data, real stakes, and real business impact. If you've spent time thinking carefully about how to measure price sensitivity at scale, how to run pricing experiments without destroying margin, or how to build forecasting systems that hold up in production — this is your role.
What You’ll Own
- Design and build production ML systems for pricing, demand forecasting, and related revenue problems
- Frame ambiguous business problems as well-defined ML tasks with clear success criteria and measurable outcomes
- Set the standard for model evaluation, validation, and monitoring — including knowing when CV metrics are misleading and when holdout testing is the only honest answer
- Build robust predictive models across classification, regression, time series, and causal inference
- Identify and prevent data leakage, overfitting, and other failure modes before they reach production
- Design and analyze experiments to measure causal impact of pricing decisions
- Debug models that fail in production — understand why they fail, not just that they do
- Translate model limitations, uncertainty, and risk clearly to both technical and non-technical stakeholders
- Partner with product, engineering, and business teams to ensure ML solutions solve real problems
Who We’re Looking For
- 7+ years of applied ML / data science experience with a track record of production systems that delivered measurable business impact.
- Deep pricing, demand forecasting, or revenue optimization experience. You've built these models, not just used them.
- Expert-level Python and SQL.
- Strong grounding in causal inference and experimental design. You know the difference between a correlation and a result.
- Ability to work with messy, real-world data and make pragmatic tradeoffs.
- Familiarity with cloud ML platforms (GCP/Vertex AI or AWS/SageMaker).
- MS or PhD in Statistics, Computer Science, Operations Research, or a related quantitative field.
- Self-directed and autonomous. You don't need the problem handed to you fully formed.
- Experience in e-commerce, retail, marketplace, or pricing-intensive industries (airlines, ride-sharing, fintech).
Who 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 justify.
- Someone who needs detailed instructions or hand-holding for ambiguous problems.
- Someone who over-engineers solutions when a simple approach would suffice.
For US-based candidates, this posting is intended for candidates that reside in the following states:
AZ, DE, FL, GA, IN, LA, MI, MS, MO, NV, NC, OK, PA, TN, TX, UT, WV, WI, and WY.
Our preference is for candidates who reside near our hubs in Northwest Indiana, Austin, Texas, and Toronto, Ontario.
Washington state applicants only: If you believe that this job posting does not comply with applicable Washington state law, please notify us by sending an email to [email protected].
Top Skills
AWS
GCP
Python
R
Sagemaker
SQL
Vertex Ai
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The Company
What We Do
We acquire overlooked store and catalogue based retailers and transform them into high performance, "digital first” brands through our proven omni-channel technology platform, operating expertise and scale.
Founded by Justin Yoshimura and backed by world class investors.
