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

Position – Senior/Lead Data ScientistLocation –RemoteType – 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 hours5+ 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.