Data Scientist, Talent Management Measurement & Insights

- 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
- 2+ 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
- Experience in Python, Perl, or another scripting language
- Experience in a ML or data scientist role with a large technology company

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.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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.
Are you passionate about using science to build disruptive solutions that challenge the status-quo? Do you want to fundamentally redefine talent management and development for one of the largest and most complex workforce in the world? If you are, we want to talk to you.

Key job responsibilities
We are looking for a Data Scientist generalist who can work with senior leadership and also partner with technical experts to deliver deliver best-in-class data science solutions for Amazon Talent. The Data Scientist will be comfortable leading statistical and ML projects from conception to production, including understanding business needs, transforming and exploring data, building and validating ML models, and deploying completed models on the AWS cloud. Finally, this person will be an expert at synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication.

A day in the life
In this role you will closely partner with Amazon WW Stores, Finance, and HR leadership teams, and with various engineering, data, and science teams across the company. Your goal will be to discover best in class approaches to help better understand and forecast talent movement and enable your customers to derive actionable insights to inform talent strategies. You will develop scientific solutions grounded in rigorous experimentation and measurement to support the growth and mobilization of our workforce, and strategic organizational planning to ensure our talent is optimally positioned to capitalize on emerging business opportunities.

About the team
The Measurement & Insights team serves as the analytical backbone of Amazon Stores Talent Management, ensuring that Amazon Stores uses state-of-the-art of data-driven talent optimization in the rapidly evolving retail and e-commerce landscape. We focus on providing scientific insights to solve durable customer problems relating to talent optimization, organizational structure, and operating mechanisms. Our interdisciplinary science team with expertise in psychometric measurement, experimental and quasi-experimental research design, statistics, and machine learning, guide the metric design and scientific approaches, uncover blind spots, and provide proactive insights to address potential challenges and opportunities.