Data Scientist, Workforce Staffing

Job summary
The Amazon Global Workforce Staffing Organization is key to delivering our customer experiences around the globe. With 1M hourly associates hired annually, and growing to more than 1m in the next 24 months, it is a key operational mechanism to ensure we deliver our customer experiences globally.

The Amazon Workforce Staffing Candidate Services (WFS CS) team is one branch of the Global Workforce Staffing Organization. WFS CS provides email, SMS, chat and phone support across more than five different languages to candidates applying for jobs with Amazon. This customer service for candidates removes technical barriers in the hiring process. Our call center agents will resolve issues so that candidates who contact our call centers end up hired, which we call a Day 1 Start (D1S). Our job is to “convert” these candidates into a D1S, hence increasing our Conversion Rate.

The ideal candidate will be an expert in the areas of science, machine learning and statics, having hands-on experience with multiple improvement initiatives. As part of this role, you will build s to improve the candidate experience. The candidate needs experience with science/business intelligence, analytics, and reporting systems while striving for simplicity, and demonstrating significant creativity and high judgment backed by statistical proof.

This role requires working closely with business, engineering and other cross functional teams within Workforce Staffing to deliver results. You will lead high visibility and high impact programs collaborating with various teams.

Key job responsibilities
• Building and automating quality and conversion rate reports for internal consumption and distributing them to vendors.
• Own the design of experiments such as A/B testing in order to assess new enhancements, pilot ideas, and validate their impact.
• Define, create and invent metrics and Key Performance Identifiers (KPIs) to measure the impacts of WFS decisions on candidate hiring experiences
• Be a storyteller and communicate the needs of candidates, associates and staffing coordinators to WFS stakeholders using candidate insights
• Ability to perform/own reoccurring and ad-hoc business intelligence projects

Basic Qualifications

• Bachelor degree in related field (CS, machine learning, mathematics, statistics) or equivalent experience.
• 2+ years of experience with scripting languages or statistical/mathematical software (e.g. R, or Matlab)
• 2+ years of experience with machine learning, statistical modeling, mining, and analytics techniques.
• Experience applying various machine learning techniques, and understanding the key parameters that affect their performance.
• Experience developing experimental and analytic plans for modeling processes, use of strong baselines, and the ability to accurately determine cause and effect relationships.
• Have a history of building systems that capture and utilize large sets in order to quantify performance via metrics or KPIs.
• Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation sets, etc.

Preferred Qualifications

• 5+ years of experience with machine learning, statistical modeling, mining, and analytics techniques.
• Previous experience in a ML or role with a large technology company
• Familiarity with Amazon's cloud infrastructure including EC2, S3 and Redshift
• Develops strong relationships across teams
• Highly organized individual who also wants to have fun building new products and services across geographies
• Ability to work on a diverse team or with a diverse range of coworkers



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.