Lead Applied Scientist, Personalization & Recommendations

Amazon Inc.; Seattle (WA); Presencial; USD 143.000 - 193.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health insurance

401(k) matching

Paid time off

Parental leave

RSUs

Descripción de la vacante

Amazon Advertising is seeking a lead ML scientist to shape the applied science roadmap for recommendation and personalization. You will work with cross-functional teams to deliver ML-driven solutions that improve advertiser experience and revenue across the ad console.

The role involves hands-on modeling, end-to-end ML project leadership, and collaboration with software engineers to productionize models, with opportunities to advance the science of advertising at scale.

Formación

• 3+ years building models for business applications.
• PhD, or MS with 5+ years in CS/CE/ML or related field.
• Proficient in Java, C++, Python.
• Experience with algorithms, data structures, numerical optimization, data mining, HPC.

Responsabilidades

• Serve as technical leader in ML, guiding collaboration between scientists and engineers.
• Perform hands-on analysis and modeling on large-scale data to boost ads revenue.
• Lead end-to-end ML projects with ambiguity, scale, and complexity.
• Build, experiment, optimize, and deploy ML models into production.
• Run A/B experiments, analyze data, and validate models statistically.
• Develop scalable automated data analysis, model development, and serving processes.
• Explore innovative ML approaches to push boundaries.

Conocimientos

ML modeling

Python

Java

C++

Algorithms

Data mining

Educación

PhD or MS + 5+ years CS/ML

Herramientas

Unix/Linux

Descripción del empleo

Amazon Advertising is seeking a lead ML scientist to shape the applied science roadmap for recommendation and personalization. You will work with cross-functional teams to deliver ML-driven solutions that improve advertiser experience and revenue across the ad console.

The role involves hands-on modeling, end-to-end ML project leadership, and collaboration with software engineers to productionize models, with opportunities to advance the science of advertising at scale.