Forecasting Lead Data Scientist

The candidate must be 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.

Required:

• Must be able to work EST hours

• 8+ 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: R², 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.