Data Scientist - AI Platform

Workday•Atlanta, GA

1d•$130,000 - $219,400•Hybrid

About The Position

As a Data Scientist, you'll lead cross-functional data science efforts to understand business requirements and design, build, and implement innovative solutions that enhance statistical modeling, measure value delivery and inform product decisions. The bulk of the work will be in machine learning (ML) modeling, management and problem analysis, data exploration and preparation, data collection and integration and operationalization. This role offers ground-floor enterprise impact, as you will be joining at a pivotal inflection point where the Platform Consumption Console (PCC) is rapidly transitioning from a localized billing tool into a core piece of Workday’s enterprise infrastructure. The work you do here will directly shape how Workday monetizes its products for years to come. You will tackle complex, high-stakes engineering and product challenges—from orchestrating the ingestion of massive daily usage metrics and ensuring zero-defect financial ledgers, to building intuitive, high-visibility dashboards for our enterprise customers. The culture is described as a "Startup Within a Giant," transitioning to a highly focused, domain-driven platform model, ideal for builders who want to define strategy, establish new technical standards, and see the immediate impact of their work.

Requirements

5+ years of hands-on experience in Data Analysis and visualization.
5+ years of experience programming in Python, R, and SQL.
Strong experience with popular database programming languages including SQL, PL/SQL for relational databases is required.

Nice To Haves

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.
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.
Knowledge of SaaS business preferred.
Experience in the B2B software industry is preferred.
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.

Responsibilities

Lead cross-functional data science efforts to understand business requirements.
Design, build, and implement innovative solutions that enhance statistical modeling.
Measure value delivery and inform product decisions.
Machine learning (ML) modeling, management and problem analysis.
Data exploration and preparation.
Data collection and integration.
Operationalization of ML models.
Orchestrate the ingestion of massive daily usage metrics.
Ensure zero-defect financial ledgers.
Build intuitive, high-visibility dashboards for enterprise customers.