Leveraging AI/ML to enhance decision-making, automate complex processes, and unlock actionable insights across Workday to improve efficiency, optimize workflows, and empower various Workday teams.
Requirements
- 5+ years experience with a specialization in ML engineering or data science
- 4+ years of experience with Databricks
- 4+ years of relevant hands-on experience in successfully launching, planning, executing machine learning projects.
- Experience in applying ML and data science to support business functions such as Sales, Marketing, Services, Support and Finance.
- Experience in one or more of the following commercial/open-source ML framework/tools: Amazon SageMaker, Python/R, RapidMiner, Alteryx, H2O, TensorFlow.
- 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.
- Strong experience with popular database programming languages including SQL, PL/SQL for relational databases is required
Responsibilities
- Prioritize, scope and manage machine learning projects and the corresponding key performance indicators (KPIs) for success.
- 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.
- Identify data-driven/ML business opportunities.
- Understand new data sources and process pipelines for both structured and unstructured data.
- Collaborate with Data Engineers and other Business Technology(BT) teams to evaluate and implement ML deployment options.
- Establish best practices around ML production infrastructure.
- Closely work with the Pune team for CI/CD of our AI products.
Other
- Display drive and curiosity to understand the business process to its core.
- 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.
- Promote collaboration with other data science teams within the enterprise, encourage reuse of artifacts.
