Senior Data Scientist, Managed Operations & ML Pipelines

Amazon Web Services (AWS); Arlington (VA); On-site; USD 136,000 - 184,000; Full time

Job summary

Amazon Web Services (AWS) is the world leader in cloud infrastructure, seeking a Full Stack Data Scientist to drive data-driven transformation. You will manage the end-to-end lifecycle from data exploration to model deployment and visualization, leveraging Spark, Airflow, Hugging Face, and other ML frameworks.

You will collaborate with cross-functional teams to create scalable analytics solutions, build robust data pipelines, and define KPIs that reflect strategic objectives.

Qualifications

• 2+ years of data scientist experience.
• 3+ years SQL or Python/R/SAS/Matlab experience.
• 3+ years ML/statistical modeling experience.
• 1+ years guiding researchers.
• 1+ years evaluating AI systems.
• Master's degree in STEM.
• Experience applying theoretical models in applied environments.

Responsibilities

• Work with large and complex data sets to solve a wide array of challenging problems using different analytical approaches.
• Develop ML/AI models. Partner with software teams to productionalize these models.
• Data Pipeline and Infrastructure: design and implementation of data pipelines
• Metric Development and Monitoring: Define and develop advanced, customized metrics and KPIs that capture the nuances of the organization's strategic objectives and operational complexities.
• Continuously monitor and evaluate the performance of metrics.

Skills

Data science

SQL

Python

R

SAS

Matlab

ML/Modeling

Data visualization

Education

Master's degree in STEM

Ph.D. in STEM (preferred)

Tools

Spark

Airflow

Hugging Face

Job description

Amazon Web Services (AWS) is the world leader in cloud infrastructure, seeking a Full Stack Data Scientist to drive data-driven transformation. You will manage the end-to-end lifecycle from data exploration to model deployment and visualization, leveraging Spark, Airflow, Hugging Face, and other ML frameworks.

You will collaborate with cross-functional teams to create scalable analytics solutions, build robust data pipelines, and define KPIs that reflect strategic objectives.