Senior Data Scientist, BEAM - Competitiveness Metrics

Amazon; Seattle (WA); In loco; USD 159.000 - 215.000; Tempo pieno

Vantaggi offerti da questo lavoro

RSUs

Health insurance

401(k) matching

Paid time off

Descrizione del lavoro

Amazon BEAM is seeking a Senior Data Scientist to anchor the Topline View of Retail. You will define competitiveness metrics, build scalable statistical and ML models, and fuse third‑party data with internal signals to explain share of wallet across segments and time.

You will produce monthly reports and narratives for CXBT and senior leadership, mentor teammates, and defend methodologies to VPs with crisp, two‑sentence explanations.

Competenze

• Master's degree or PhD in a quantitative field.
• Experience applying statistical modeling and ML methods to business problems.
• Proficiency with SQL and Python or R for large datasets.
• Demonstrated ability defining metrics and KPIs from scratch.

Mansioni

• Own the definition and evolution of BEAM's competitiveness metrics.
• Design, build, and validate statistical and ML models.
• Integrate third-party panel data with internal signals into reproducible pipelines.
• Translate business questions into defensible metrics and models.
• Productionize models for recurring reporting with data engineers.
• Produce monthly flash reports and deep-dive narratives for leadership.
• Mentor other scientists and uplift modeling rigor.

Conoscenze

Statistical modeling

Machine learning

Metrics design

Executive communication

Formazione

Master's or PhD in a quantitative field

6+ years data science experience

Strumenti

SQL

Python

R

AWS

Spark

Descrizione del lavoro

Amazon BEAM is seeking a Senior Data Scientist to anchor the Topline View of Retail. You will define competitiveness metrics, build scalable statistical and ML models, and fuse third‑party data with internal signals to explain share of wallet across segments and time.

You will produce monthly reports and narratives for CXBT and senior leadership, mentor teammates, and defend methodologies to VPs with crisp, two‑sentence explanations.