HR Data Scientist

at  Capgemini

QUALIFICATIONS

• Strong level of knowledge acquired over time in a professional HR analytics/data science position, a minimum of 5 years of experience.
• Experience with the field of People Analytics specifically, of an analytical / business partnering role in a People / HR function or with a human / psychology / workplace element;
• Extensive experience applying statistical, data science and advanced analytics techniques to answer business questions, incl. using storytelling and having the ability to explain complex and contentious subject matter;
• Strong knowledge, ability to apply and explain statistical concepts and techniques, both descriptive and inferential, incl. modelling, time-series analysis, with the ability to select the most appropriate techniques for a given use case;
• Ability to recognize and interpret business requirement and translate this need into technical design of an analytics solution;
• Architectural and/or feature knowledge of one or more of the listed Languages or Packages: Python, R, MATLAB, STATA, SPSS, SAS, PowerBI, Tableau;
• Experience applying advanced statistical methods and techniques;
• Evidence of post qualifying and continuing professional development;
• Detail-oriented expert with a strong analytical skill;
• Desirable: French/German language speak to a high level of fluency.

• Provide leadership and advisory support to clients looking to develop their practice around People Data and Analytics and deepen the way in which data enhances their People/HR function;
• Work with clients on their HR Analytics and Research needs, design and deliver data science projects;
• Develop analytical tools and products that use a range of structured and unstructured data sets from both people and non-people systems to make predictions in critical areas for clients’ People Experience;
• Develop long-term mutually beneficial relationships with clients that allow anticipation of client requirements and an advisory style relationship;
• Develop data visualisations that support clients to tell stories with data, garnering support for projects and initiatives that are indicated by the data for prioritisation;
• Coach, mentor and develop junior resources;
• Deliver work in a structured manner, finding a balance between creativity and practicality to ensure we meet client standards efficiently within the agreed timescales;
• Drive innovation and continually develop the team’s knowledge and own knowledge in the areas of statistics, People Analytics, Machine Learning, AI and related topics.