Description

Are you interested in using data and science to solve the largest scale problems in AWS Data Center Global Operations? Do you want to play a critical role in developing the future of repair in Data Center Operations through Machine Learning? Come join us!

The Central Infrastructure Analytics Team (CIAT) Sr. Applied Scientist transforms data into actionable insights for global teams by 1) interpreting enterprise scale data sets from a variety of internal sources to uncover the functional activity and implications, 2) analyzing this data to discover patterns, trends and correlations, 3) developing hypotheses and assisting in the design of experiments to explore these hypotheses, and 4) developing and deploying actionable ML models and business intelligence solutions for global customers.

CIAT collects data from diverse sources of internal systems which often require cleaning, interpretation, and combination in order to tell a functional story. The Applied Scientist role is critical in transitioning the analysis output from Descriptive/Diagnostic to Predictive/Prescriptive, and providing the operations teams with actionable insights to enable ongoing improvements. The Applied Scientist will use a variety of tools (e.g. Python, SQL, SageMaker, R, SAS, etc.) to deep dive data sources to discover useful patterns that will drive process improvement or remediate systemic issues.

Key job responsibilities

• Design, develop, and evaluate innovative ML models to solve diverse challenges and opportunities across data center global operations

• Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity.

• Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production.

• Work with a scientists and software engineers to deliver machine-learning and data science solutions to production.

• Perform hands-on data analysis, employ statistical testing methods and strategies, run regular A/B tests, and clearly communicate the impact to technical and non-technical audiences in senior leadership.

• Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.

In this role you will apply advanced analysis techniques and statistical concepts to draw insights from enterprise scale datasets, build scalable machine learning models, and create intuitive data visualizations. You will contribute to each layer of the data solutions, working closely with Data Scientists, Engineers, Business Intelligence Engineers, and Global Process Owners to understand the business objectives, obtain relevant datasets and build prototype predictive and prescriptive analytic models. You will review key results with business leaders and stakeholders, and you will work with your team to develop and deploy a productionized version of the model to your global customers.

About the team

The Central Infrastructure Analytics Team (CIAT) provides critical business intelligence services across a broad range of functions within the AWS global Data Center Community (DCC). Situated within Central Operations, CIAT is the analytics hub for Data Center based organizations, including but not limited to: operations, logistics, engineering and equipment management, safety, and security. CIAT is comprised of several specialty Builder functions including data engineering, business intelligence (visualization), systems engineering, and data science. We build business intelligence solutions that drive the right actions at scale across our global data centers and supporting services.

We are open to hiring candidates to work out of one of the following locations:

Herndon, VA, USA

Basic Qualifications

• 3+ years of building machine learning models for business application experience

• PhD, or Master's degree and 6+ years of applied research experience

• Experience programming in Java, C++, Python or related language

• Experience with neural deep learning methods and machine learning

Preferred Qualifications

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.

• Experience with large scale distributed systems such as Hadoop, Spark etc.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.