Senior/Lead Data Scientist (Client: Freelance) [Location: Remote]

 Freelance

·

Remote

12000 employees

Job Description

– 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.