People Analytics Data Scientist at Gartner, Inc. in Stamford, Connecticut

Job Description:

What makes Gartner HR a GREAT fit for you? When you join Gartner, you'll join a fast-paced, dynamic team. We're a people business. From talent acquisition and management to compensation, benefits, analytics and operations, finding and keeping the right people is the core of our strategy for success. You'll work alongside smart, creative, motivated colleagues and have unlimited opportunities to develop in your career. If you love working with people and making the connection between great talent and company success, we want to connect with you.

Interested in learning more, view and register for any of our upcoming recruiting events here !

Gartner's core asset is its people.  Our people develop insights, share advice, and create experiences that help our clients be more successful.  The People Analytics team derives actionable insight from data to recommend and implement better ways of attracting, developing, and retaining our talent so that we can drive higher sales, more productivity, and better client experiences.  We drive impact on Gartner's Mission Critical Priorities by leveraging state of the art technology, analytics, and processes.

In the People Analytics Data Science group (PADS) our goal is to help deliver on the promises of People Analytics by building and deploying predictive and descriptive models based on observable data. We are creating a scalable Machine Learning Operations (MLOps) environment to allow us to quickly, efficiently, and ethically build models which can augment our employee, recruiter, and candidate experiences.

The goal of the Pod Member in PADS is to help deliver model-building projects to the business using their technical skills as well as their inquisitive nature. This is a complex role Machine Learning, Data Extraction, Data Exploration, Statistics skills. A combination of these skillsets will allow the applicant to help the team deliver accurate and ethical results on time. The Pod Member will be asked to justify their design decisions and participate in challenging model purposes as well as efficacy.

Major Responsibilities:

• Work with an HR Data Science Pod to help deliver key predictive and inferential models to the business.


• Create and execute against a project plan that includes:
  
  
  • building models or other data-driven products that help address those business needs, and
  
  
  • maintain and deploy developed models; and
  
  
  • interrogate data sources and modeling approaches with ethics and bias in mind; and
  
  
  • quickly understanding new data sources and finding quick ways to extract information from them for modeling and inference purposes; and
  
  
  • working with technical teams to bring developed models from exploration to deployment.
  
  
  
  


• Application of Machine Learning and Statistical methods which will be the most useful for HR processes.


• Testing models for fairness and bias and being conscious of the many ways in which bias can affect processes.


• Using an MLOps infrastructure to help build, deploy, and monitor created models.


• Understand business problems and translate them into models that drive result

Undergraduate or master's degree in any of:

• Computer Science/Engineering,


• Electrical Engineering,


• Physics,


• Engineering,


• Mathematics,


• Statistics,


• Data Science


• Adjacent topics (STEM or Mathematical degrees),

Equivalent business experience

Minimum of 2+ years work experience, of which include:

• 1-2 Years of relevant technical work experience

Required:

• Integrity


• Experience working with confidential/sensitive information


• Open-mindedness to stakeholder perspectives


• Machine Learning (scikit-learn, PyTorch, Keras, etc.. )


• Data Analysis (pandas)


• Data Extraction


• Programming proficiency in Python, R a plus


• Comfort working in command lines

Preferred:

• AzureML experience


• Power BI


• Statistics


• SpaCy

Job Requisition ID:52135

By submitting your information and application, you confirm that you have read and agree to the country or regional recruitment notice linked below applicable to your place of residence.

Gartner Applicant Privacy Link: https://jobs.gartner.com/applicant-privacy-policy

For efficient navigation through the application, please only use the back button within the application, not the back arrow within your browser.