Data Scientist II, AWS Managed Operations Data Science (MODS)

Amazon•Arlington, VA

42d

About The Position

Amazon Web Services (AWS) is the world leader in providing a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world! Passionate about building, owning and operating massively scalable systems? Want to make a billion-dollar impact? If so, we have an exciting opportunity for you. The AWS Managed Operations (MO) organization was founded in April 2023, with the objective to reduce operational load and toil through long-term engineering projects. MO is building the best-in-class engineering and operations team that will own the day-to-day operations for AWS Regions; improving the availability, reliability, latency, performance and efficiency to operate AWS regions. The AWS Managed Operations Data Science (MODS) Team is looking for a Data Scientist to lead the research and thought leadership to drive our data and insight strategy for AWS. You will be expected to serve as a Full Stack Data Scientist. You will be responsible for driving data-driven transformation across the organization. In this role, you will be responsible for the end-to-end data science lifecycle, from data exploration, ETL, model development and data visualization. You will leverage a diverse set of tools and technologies, including general analytical frameworks (Spark, Airflow, etc.), AI frameworks (Hugging Face, etc.) and various machine learning frameworks, to tackle complex business problems. Your analytics research will provide direction on the technology strategy of the Managed Operations organization. Your Decision Science artifacts will provide insights that inform AWS' Operations and Site Reliability Engineering teams. You will work on ambiguous and complex business and research science problems at scale. You are and comfortable working with cross-functional teams and systems. This role will sit in our new headquarters in Northern Virginia, where Amazon will invest $2.5 billion dollars, occupy 4 million square feet of energy efficient office space, and create at least 25,000 new full-time jobs. Our employees and the neighboring community will also benefit from the associated investments from the Commonwealth including infrastructure updates, public transportation improvements, and new access to Reagan National Airport. By working together on behalf of our customers, we are building the future one innovative product, service, and idea at a time. Are you ready to embrace the challenge? Come build the future with us. This position requires that the candidate selected be a U.S. citizen. 10012

Requirements

3+ years of data scientist experience
3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
Knowledge of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.
Bachelor's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field, or Master's degree and 4+ years of data scientist experience

Nice To Haves

6+ years of data scientist experience
4+ years of machine learning, statistical modeling, data mining, and analytics techniques experience
Experience with data scripting languages (e.g., SQL, Python, R, or equivalent) or statistical/mathematical software (e.g., R, SAS, Matlab, or equivalent)
Experience with clustered data processing (e.g., Hadoop, Spark, Map-reduce, and Hive)
Experience in a ML or data scientist role with a large technology company

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 key performance indicators (KPIs) that capture the nuances of the organization's strategic objectives and operational complexities. Continuously monitor and evaluate the performance of metrics