HR Data Scientist
Amazon’s EU HR Operations Project Management Office (PMO) is looking for a Data Scientist to be part of our Research Analytics group. As a member of the team, you will leverage established and novel data sources, quantitative and qualitative research, and machine learning techniques to deliver tools and insights 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 and technical teams 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.
· 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 leaders in HR and across the business
· Write research papers for internal audiences
· Carry out analysis in collaboration with our Program Managers to support EU HR projects and initiatives
· Participate in planning and design of research. Scope, conduct, direct, and coordinate all phases 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
· Provide expert level consulting to HR and business leaders to develop appropriate reports, metrics and research
· Master's degree in Statistics, Mathematics, Computer Science, Machine Learning or related field
· Experience analyzing large quantities of data
· Experience writing advanced SQL, data modeling, data mining (SQL, ETL, data warehousing) and using databases in a business environment with complex datasets Proficiency in a minimum of one statistical analysis tool/package: 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.
· Experience with data mapping and org design for core HCM technology such as PeopleSoft, SAP or Oracle HCM
· 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
· 5+ years industry experience, preferably within the field of HR Analytics
· Experience in database design and ETL mapping
· Project Management experience
· Advanced degree in Business Administration, Math, Computer Science, Statistics, Finance, or any related field from an accredited institution or 2+ years Amazon experience
· PeopleSoft, Oracle EBS, SAP, or Workday experience in Business Process Design and System Configuration is a plus
· 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 from initiation through delivery
· 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
· Customer obsession and bias for action
· High levels of integrity and discretion in handling confidential information
· Proven ability to influence change strategies with data. Examples where support for change occurred because of data
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