Research Scientist in People Analytics

Amazon.com

Luxembourg, LU

il y a 3j

Job Description

Amazon’s EU Human Resources (HR) Operations Project Management Office (PMO) is looking for a research scientist to be part of our Research and Analytics stream.

As a member of the team, you will leverage established and novel data sources, perform quantitative and qualitative research, and implement statistics and machine learning techniques to deliver insights and tools that have a direct impact on Amazon’s workforce.

You will be at the forefront of using data science to transform how Amazon attracts, develops, and retains the world’s best employees.

You will work closely with the business to perform compelling analysis that delivers actionable results. You will also build predictive workforce models that have a direct impact on day-

to-day decision-making and on HR project investments.

Responsibilities Include

Develop predictive models for important business- and people-centered outcomes

Design experiments to identify causal factors

Develop analysis plans and implement appropriate modeling techniques to answer complex business questions

Interpret data and communicate complex findings to the team, leaders in HR and across the business

Write research papers for internal audiences

Carry out analysis in collaboration with the PMO streams to support EU HR projects and initiatives

Participate in scoping, planning, and design of research projects

Apply appropriate techniques to collect, organize, and analyze data to generate insights

Drive the collection of new data and the refinement of existing data sources

Basic Qualifications

PhD in Industrial Organizational Psychology, Operational Research, Behavioral Economics, Statistics, Mathematics, Computer Science or related field, or Master’s degree in respective field with +4 years of experience.

Experience analyzing large quantities of data

Proficiency in minimum one statistical analysis tool / package such as : R, SPSS, SAS, Stata, Matlab, Python

Proficiency in several techniques including but not limited to : Decision Trees, GLM, Clustering, Bayesian methods, SVM, linear / non-

linear programming, Multi-level models, Random Forests, Choice Models, etc.

Commitment to rigorous testing and validation to ensure findings are consistent, accurate, and generalizable

Comfortable mining unstructured data, with the ability to transform data into a usable state using appropriate tools and techniques

Demonstrated ability to work effectively in a collaborative environment

Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner

Excellent written and verbal skills

Preferred Qualifications

Proven analytical and quantitative ability and a passion for enabling customers to use data and metrics to back up assumptions, develop business cases, and complete root cause analyses

Strong verbal and written communication and data presentation skills that allow you to clearly, compellingly, and effectively influence audiences internally and externally, across organization boundaries

Able to source, work with, and combine disparate data sets to answer business questions.

Able to deliver complex analysis / projects when neither problem nor solution is not well defined

Inquisitive technical and business skills to understand, test, or challenge the status quo while working harmoniously with the business and technology owners

Anticipate bottlenecks, provide escalation management, anticipate and make tradeoffs, and balance the business needs versus technical constraints

Experience writing advanced SQL, data modeling, data mining (SQL, ETL, data warehousing) and using databases in a business environment with complex datasets

Experience with data mapping and org design for core Human Capital Management (HCM) technology such as PeopleSoft, SAP or Oracle HCM

Project Management experience

High levels of integrity and discretion in handling confidential information

Customer obsession and bias for action

Proven ability to influence change strategies with data. Examples where support for change occurred because of data

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