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.
About the Team
The Data Science Team Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse sources to generate actionable insights and solutions for client services and product enhancement. They also interact with product and service teams to identify questions and issues for data analysis and experiments. The team develops and codes software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources. Identifies meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers.
About the Role
Workday is looking to fill the full-time position of a Senior Data Scientist. This position will support Advanced Analytics initiatives for the Workday enterprise.
The data scientist is a role in the Enterprise Architecture & Data (EA&D) within Business Technology and reports to the Director of Data Science and Advanced Analytics. He/she will play a pivotal role in planning, executing and delivering machine learning-based projects for the Workday enterprise. The bulk of the work will be in machine learning (ML) modelling, management and problem analysis, data exploration and preparation, data collection and integration and operationalization.
The newly hired data scientist will be a key interface between the EA&D and business teams for example Sales, Marketing, Services, Support and Finance teams, and teams within Business Technology. Candidates need to be very much self-driven, curious and creative.
- Guide and inspire the organization about the business potential and strategy of machine learning
- Identify data-driven/ machine learning business opportunities.
- Prioritize, scope and manage machine learning projects and the corresponding key performance indicators (KPIs) for success.
- Collaborate across enterprise to understand technology and business constraints.
- Execute machine learning lifecycle from ideation and hypothesis generation, data extraction and exploration, model building and validation, results communication, and productization to optimize go-to-market strategy.
- Understand new data sources and process pipelines, and catalog/document them. Display drive and curiosity to understand the business process to its core. Network with domain experts to better understand the business mechanics that generated the data.
- Have deep knowledge of fundamentals of machine learning, data mining and statistical predictive modeling, and extensive experience applying these methods to real world problems. Able to integrate domain knowledge into the ML solution. Have extensive experience in model testing, such as cross-validation and A/B testing.
- Collaborate with data engineers and Business Technology team to evaluate and implement ML deployment options. Establish best practices around ML production infrastructure.
- Promote collaboration with other data science teams within the enterprise, encourage reuse of artifacts. Train business teams on basic data science principles and techniques.
- Keep abreast of the latest developments in the Data Science field by continuous learning and proactively champion promising new methods relevant to the problems at hand.
Education and Training
- Master’s degree in computer science, data science, operations research, statistics, applied mathematics, or a related quantitative field is preferred or equivalent on-the-job experience. Alternate experience and education in equivalent areas such as economics, engineering or physics, is acceptable. Experience in more than one area is strongly preferred.
- Candidates must have a specialization in ML, AI, cognitive science or data science.
- Substantial expertise in solving propensity to buy, segmentation, next-likely purchase/tactic, time series forecasting, churn and retention analysis, text analytics problems is preferable
- Candidates should have six or more years of relevant hands-on experience in successfully launching, planning, executing machine learning projects. Preferably in the domains of segmentation, cross-sell/upsell propensity modeling, next-likely purchase/tactic modeling, time series forecasting, churn and retention analysis, text analytics/NLP, statistical methods, experimental techniques, etc.
- Candidates need to demonstrate that they were instrumental in launching significant data science projects, that they can manage large data science projects and collaborate cross-functionally with diverse teams.
- The candidate will exhibit significant project experience in applying ML and data science to support business functions such as Sales, Marketing, Services, Support and Finance.
- The candidate should be adept in agile methodologies and well-versed in applying DevOps/MLOps methods to the construction of ML and data science pipelines.
Machine Learning and Data Science Knowledge/Skills
- Substantial experience in one or more of the following commercial/open-source ML framework/tools: Amazon SageMaker, Python/R, RapidMiner, Alteryx, H2O, TensorFlow.
- Substantial expertise in solving propensity to buy, segmentation, next-likely purchase/tactic, time series forecasting, churn and retention analysis, text analytics problems is preferable.
- Knowledge and experience in statistical and data mining techniques: generalized linear model (GLM)/regression, random forest, boosting, trees, text mining, hierarchical clustering, deep learning, etc.
- Substantial coding knowledge and experience in one or more languages: for example, Python/Jupyter, R, SAS, Scala, Excel, MATLAB, SPSS, C++, etc. Exposure to other programming languages, such as Java, Go is a plus.
- Strong experience with popular database programming languages including SQL, PL/SQL for relational databases is required, exposure to non relational databases such as NoSQL/Hadoop-oriented databases such as MongoDB, Cassandra, others is a plus.
- Experience with distributed data/computing tools: MapReduce, Hadoop, Hive, Kafka is a plus.
- Experience of working across multiple deployment environments including cloud, on-premises and hybrid, multiple operating systems and through containerization techniques such as Docker, Kubernetes, AWS Elastic Container Service, and others is a plus.
- Knowledge of SaaS business preferred
- Experience in B2B software industry preferred
Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans.