Sr / Lead Data Scientist

• Able to name the industry and the outcome variable for each such engagement. Retail same-store analysis is the classic form; the analogue here is comparing similar schools and events rather than following one trend line.
• Presents to non-statisticians: business outcome first, method second; confidence stated in plain language; explicitly states what the forecast cannot do; never opens with an undefined statistical term.
• Can teach the method to a client team, not only execute it.
• Participate actively in stand-ups and backlog refinement, engage business stakeholders directly, understand why the business is asking a question, and challenge or refine the request when it is wrong.
• Strategic recommendations are expected alongside hands-on delivery.

Qualifications

Required

• Must be able to work EST hours
• 5+ years of applied forecasting.
• Two or more comparable forecasting engagements led start to finish.
• Comparable-unit / same-store forecasting experience.
• Executive communication.
• Thought leadership.
• Multivariable regression, plus collinearity analysis and VIF interpretation.
• Forecast model development, tuning, selection and holdout validation.
• Metric fluency: R , WAPE, MAPE, p-values - and why WAPE is used at event grain (many events sell zero, which breaks MAPE).
• Sparse and zero-inflated data. Many variables populate on under 25% of events, some as low as 10%. Nulls must never be silently treated as zeros.
• Data-leakage discipline and point-in-time correctness: every feature must exist before the event starts.
• Python and SQL; reproducible notebooks.
• Snowflake, including Snowflake ML Model Registry (model versions carry metrics and training-dataset references).
• Git and pull-request workflow; all code merged to the client repository, no private forks.

Preferred

• Architecture Decision Records (ADRs) and written process documentation.
• Categorical encoding at scale (~30-35 source variables expand to ~70 columns).
• Sports, streaming, ticketing or subscription-business domain exposure.
• Hierarchical or mixed-effects models for low-volume segments.

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