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# Forecasting Lead Data Scientist

- **Company:** Helishores Inc
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Encodings, Python (Programming Language), Machine Learning, SQL Databases, Snowflake, Git
- **Published:** August 14, 2026
- **Apply:** https://www.dice.com/job-detail/30e95878-28eb-4fd5-ad77-2e75f32d55e1

## About the Role

Required:

 * Must be able to work EST hours

 * 8+ 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.

## 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.


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