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:

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

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