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

Remote • Posted 6 hours ago • Updated 6 hours ago
Contract W2
Contract Independent
12 Months
No Travel Required
Remote
Depends on Experience
Fitment

Dice Job Match Score™

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Job Details

Skills

  • Machine Learning (ML)
  • SQL
  • Retail
  • Python
  • Regression Analysis
  • Snow Flake Schema
  • Forecasting
  • WAPE
  • MAPE
  • p-values

Summary

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:
  • 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.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
  • Dice Id: 90769319
  • Position Id: 9056951
  • Posted 6 hours ago
Contact the job poster
YK

Yatin Khatter

Recruiter @ SanKar Inc
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