Remote position || Senior/Lead Data Scientist (Forecasting Exp.))

Position – Senior/Lead Data Scientist Location –Remote Type – Contract /Contract to Hire Job Description • 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. 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: 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.