Sr/Lead Data Scientist

Job Summary

Senior/Lead Data Scientist

Location: Remote

Responsibilities

• 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: R, 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.

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.