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Job Description
About the Role
As a People Analyst, you will report to the Chief of Staff to the Chief Human Resources Officer. You will be centered around principles of designing for scalability and flexibility while leveraging strong data governance and data quality practices to ensure we have rich and reliable human capital data.
You will be part of a team that crafts actionable insights based on the data gathered and assessed. You will play an important role to systematize and scale existing metric catalogs and develop potential metrics we could activate for forward looking/predictive analytics relative to supporting the organizational goals to attract, engage, retain, and develop talent.
Responsibilities:
- You will partner with HR Business Partners and other company leaders, providing expertise in people data analytics.
- You will utilize your curious mindset to understand critical data factors such as the what, why, audience, context and connections associated with the data before analyzing.
- You will respectfully challenge the pursuit of data if there is no clear goal.
- You will project manage and deliver easily-digestible analysis for cyclical programs, new projects, and ad hoc requests.
- You will promote a data-driven culture, vested in deeply understanding the processes that allow workforce data capture to build and sustain high-level data quality of our talent-related information.
- You will collaborate with team members to draft actionable insights, resolve working assignments, partner in facilitating conversations with reports and metrics the team produces and developing recommendations based on findings and business context.
Basic Qualifications:
- 3+ years of relevant professional experience in an analytical role.
- 3+ years of experience working in a collaborative environment on assignments of complex scope.
- Proficiency in Workday HCM or a similar SAS system.
- You have a love for data cleaning, organizing, and performing analysis.
- You have demonstrated expertise of data visualization tools such as Tableau, Visier, SPSS.
Preferred Qualifications:
- R or Python experience.
- Bachelor’s degree in Applied Statistics/Analytics or other quantitative fields of study.
- 1+ years of experience working in a human resources department.
- Application of multivariate statistical methods such as GLM, longitudinal analysis, classification, dimension reduction, clustering, and hierarchical linear modeling among others.
The annual base pay range for this role is between $130,000.00 and $140,000.00.
The New York Times is committed to a diverse and inclusive workforce, one that reflects the varied global community we serve. Our journalism and the products we build in the service of that journalism greatly benefit from a range of perspectives, which can only come from diversity of all types, across our ranks, at all levels of the organization. Achieving true diversity and inclusion is the right thing to do. It is also the smart thing for our business. So we strongly encourage women, veterans, people with disabilities, people of color and gender nonconforming candidates to apply.
The New York Times Company is an Equal Opportunity Employer and does not discriminate on the basis of an individual's sex, age, race, color, creed, national origin, alienage, religion, marital status, pregnancy, sexual orientation or affectional preference, gender identity and expression, disability, genetic trait or predisposition, carrier status, citizenship, veteran or military status and other personal characteristics protected by law. All applications will receive consideration for employment without regard to legally protected characteristics. The New York Times Company will provide reasonable accommodations as required by applicable federal, state, and/or local laws. Individuals seeking an accommodation for the application or interview process should email reasonable.accommodations@nytimes.com. Emails sent for unrelated issues, such as following up on an application, will not receive a response.
The Company will further consider qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable "Fair Chance" laws.
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