Senior Data Scientist: Audit and Analytics

Workday, Inc.
Pleasanton, California, United States
Competitive salary
23 Feb 2021
25 Feb 2021
Job role
Do what you love. Love what you do.

At Workday, we help the world's largest organizations adapt to what's next by bringing finance, HR, and planning into a single enterprise cloud. We work hard, and we're serious about what we do. But we like to have fun, too. We put people first, celebrate diversity, drive innovation, and do good in the communities where we live and work.

Job Description

Workday, Inc., a leader in enterprise cloud applications for finance and human resources, is seeking to fill an exciting data science role on the internal audit team. The Senior Data Scientist will report to the Director of Analytics and build machine learning models to support the execution of audit activities with an emphasis on identifying anomalies, risks, and gaps in business processes. The bulk of the work will be in building machine learning (ML) models, data exploration, data annotation, and model operationalization. Additionally, this role will also be involved in auditing algorithm development with a focus on trust, reliability, accuracy, and fairness of model results. The Senior Data Scientist will be a key interface between Internal Audit, Business, and Workday Product Teams.

Primary Responsibilities:
  • Build and implement machine learning models focused on identifying anomalies, non-compliance, and anomalous behavior in business transactions.
  • Audit, evaluate, and test the performance of machine learning models using cross-validation techniques and similar.
  • Develop and evangelize risk and fraud prediction machine learning use cases that contribute to product development initiatives.
  • Execute the ML life cycle from ideation to hypothesis generation, data extraction and exploration, model building and validation, results communication, and operationalization.
  • Train internal auditors on data science, AI and machine learning principles and techniques.
  • Collaborate with Data Engineers and Business Technology teams to evaluate and implement ML deployment options.
  • Partner with internal audit members on ad-hoc analysis in support of audit and investigations.
  • Generate actionable insights through models that are fair, responsible, explainable, and trustworthy.

Requirements and Qualifications:
  • Master's degree in computer science, data science, statistics, applied mathematics, or cognitive science is strongly preferred.
  • Candidates must have 3+ years of hands-on experience in successfully executing machine learning projects. Preferably in the domains of anomaly and fraud detection, statistical methods, experimental techniques, or similar.
  • Candidates must demonstrate a solid understanding of machine learning fundamentals, statistical predictive modelling, and experience applying these methods to real world problems.
  • Candidates must have deep experience in techniques such as model cross-validation and A/B testing.
  • Candidates must be an expert in Python and its packages for data analysis (scipy, pandas, matplotlib, numpy, etc.)
  • Candidates must have strong knowledge of Deep Learning Frameworks: Tensorflow, Keras, Pytorch, etc.
  • Experience in Natural Language Processing using deep learning (RNN, CNN, LSTM, etc.) is a strong plus.
  • Experience in one or more container ecosystems (Docker, Kubernetes, Mesos) is a plus.
  • Candidates must demonstrate the ability to work in diverse, cross-functional teams in a dynamic business environment.