CSC Generation - Staff Data Scientist– Pricing Science
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Requirements
• 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 • 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 • 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 • 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
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
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