American Express Careers
Manager, Human Capital Insights (HR Data Science)
The Human Capital Insights team provides actionable insights and analytics thought leadership to power HR decision-making and advance a data-driven HR culture.
The Manager, HR Data Science will be responsibile of analyzing internal and external data to inform strategy and decision making for the Global Talent, Leadership and Learning organization. S/he will have influence in shaping the evolution of HR analytics within American Express and building and evolving our analytics processes and capabilities.
- Apply Machine Learning to solve business problems related to Human Resources, for example, understanding the dynamics of career development, drivers of attrition, survey analysis, etc.
- Involvement in the complete model development cycle: business problem definition, data collections and processing, model design, model development and validation, documentation, synthesize conclusions into recommendations, and communicate results with senior leadership within HR and across the organization.
- Keep up to date with the latest developments in Machine Learning to identify opportunities for their use in HR problems
- Coordinate with cross-functional teams including HR and technologies to manage projects to completion.
- A degree on a STEM field such as Statistics, Mathematics, Physics, Engineering, or Computer Science.
- 3 plus years of programming experience (coursework or work-experience) in at least two of the following languages: C, Java, Python, R, Matlab, SAS. Familiarity with statistical and numerical packages in Python (Scikit-Learn/Tensorflow/Keras) in a UNIX environment is a plus.
- Strong mathematical/statistical background with knowledge on multivariate calculus, linear algebra, optimization, statistics, and probability. Familiarity with mathematical analysis, stochastic processes, ensemble methods, and deep learning is a plus.
- Passion for learning to keep up with the latest developments in Machine learning including algorithms and software implementations.
- Excellent communication and presentation skills: ability to distill and present actionable and clear information from complex research.
- Track record of partnering, building relationships, and team work within a matrix environment.
- Master’s degree in Mathematics, Statistics, Computer Science, Engineering, Physics, Finance, Economics, or related quantitative field.
Schedule (Full-Time/Part-Time): Full-time