Description

Amazon Web Services (AWS) is seeking a experienced Data Scientist to build data products for the Professional Services (ProServe) - Operations Technology - Data Science and Engineering team. This is an unique opportunity to think big, insist on the highest standards, and invent and simplify the data products to scale and accelerate our enterprise customers' journey to the cloud. The Data Science and Engineering team builds advanced analytical products, including feature engineering, predictive and prescriptive modeling, and generative AI application development for internal customers.

AWS provides companies of all sizes with an infrastructure web services platform in the cloud (“cloud computing”). With AWS you can requisition compute power, storage, and many other services – gaining access to a suite of elastic IT infrastructure services as your business demands them. AWS is the leading platform for designing and developing applications for the cloud and is growing rapidly with millions of customers in over 190 countries. Many of these customers seek help from AWS Professional Services in their journey to a cloud-based IT operating model.

Do you have expertise in a range of data science methodologies and have a track record of developing machine learning (ML) models to answer business questions at scale? Do you live and breath algorithms that lead to predictive and prescriptive analytics? In this role, you will apply scientific principles to business problems analyzing complex data sets to make rapid decisions for practice development and operations leaders. You will create visualizations and develop models to drive data insights and scale algorithms. In partnership with engineers, analysts and business owners, you will work backwards from business objectives to drive scalable solutions with statistical and machine learning models. You will be a technical leader influencing the analyses and best practices across multiple teams. Above all, you should be passionate about working with data to answer business questions and drive growth.

Key job responsibilities

• Demonstrate thorough technical knowledge on feature engineering of large datasets, effective exploratory data analysis, and model building using industry standard AI/ML models and working with Large Language Models.

• Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area.

• Proficiency in both supervised and unsupervised algorithms to build predictive and prescriptive solutions for AWS Professional Services organization.

• Understand the business reality behind large sets of data and develop meaningful solutions comprising of analytics as well as marketing management.

• Conduct end-to-end AI/ML project, including working backwards from customer pain-point, researching project objective, building and evaluating models, and communicating project results to stakeholders.

• Proficiency with dataset creation and management and passionate about working with huge data sets ( training/fine tuning)

• Exposure at implementing and operating stable, scalable data flow solutions from production systems into end-user facing applications/reports. These solutions will be fault tolerant, self-healing and adaptive.

About the team

The ProServe Strategy & Operations - Operations Technology team delivers relentless innovation that accelerates smarter decisions for a better Professional Services through technology, automation, and advanced analytics. Our mission is to provide AWS with the right information at the right time to make analytically-informed decisions about business performance and desired outcomes. The team supports AWS Professional Service's mission by ensuring that our data are trusted and secured via business systems and automation technologies to deliver actionable insights that drive business growth and efficiencies.

About AWS

Diverse Experiences

AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Why AWS?

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Inclusive Team Culture

Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.

Mentorship & Career Growth

We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Work/Life Balance

We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

Hybrid Work

We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our US Amazon offices.

Basic Qualifications

• 2+ 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

• Experience applying theoretical models in an applied environment

Preferred Qualifications

• Experience in Python, Perl, or another scripting language

• Experience in a ML or data scientist role with a large technology company

• Experience with AWS technologies like Redshift, S3, EC2, SageMaker, Glue, Bedrock

• Experience using ML libraries, such as scikit-learn, caret, mlr, or mllib

• Experience with business intelligence and data visualization and reporting tools (e.g. Tableau, QuickSight, etc)

• Experience forecasting in sales and/or professional services

• Experience writing and presenting complex technical concepts to broad audiences

• Ability to manage competing priorities in an ambiguous environment

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.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $125,500/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.