İK Analitiği Uzmanı

We are seeking an HR Analytics Specialist to help drive data-informed decision-making across our human resources processes. This role is responsible for collecting, analyzing, and interpreting HR-related data to generate practical insights, improve HR practices, and support strategic decision-making. The ideal candidate is analytical, and passionate about leveraging data to enhance the employee experience and organizational performance.

• Collect, clean, and analyze data from various HR systems and sources

• Conduct workforce analytics on topics such as workforce planning, talent management, recruitment, engagement, and performance

• Track and report on HR KPIs, and regularly present dashboards and insights to management

• Develop custom reports and analyses based on the needs of HR and other departments

• Measure the effectiveness of HR strategies and recommend improvements based on data

• Analyze survey data from employee engagement and exit interviews to identify trends and areas of improvement

• Ensure data privacy and compliance with relevant regulations (e.g., GDPR, KVKK)

• Stay up to date with the latest HR analytics tools and trends, and continuously improve analysis processes

• Bachelor's degree in Industrial Engineering, Statistics, Mathematics, Economics, Business Administration, Psychology, Human Resources, or a related field

• Minimum 2 years of experience in HR analytics, data analysis, or business intelligence

• Proficiency in Excel, Power BI and/or Tableau for data visualization

• Experience in SQL and data querying (preferred)

• Familiarity with data analysis in Python or R (preferred)

• Strong analytical thinking, problem-solving skills, and communication abilities

• Solid understanding of HR processes and key HR metrics

• Conscientious, with a strong sense of confidentiality and data ethics

• Preferred Skills:

  • Experience with HR systems such as Workday, SAP SuccessFactors, or Oracle HCM

  • Knowledge of advanced analytics techniques such as data modeling, regression analysis, and predictive analytics

  • Experience working in Agile environments