Develop and deploy analytic models for workforce demand forecasting, resource optimization, and capacity planning.
Implement workforce allocation algorithms to optimize staffing and resource allocation.
Design and develop models/algorithms based on predictive/descriptive statistical modeling, machine learning, and LLMs/AI to glean insights from large datasets.
Continuously monitor and implement data quality processes to ensure accuracy, consistency, and reliability.
Clean, transform, and optimize data for analysis.
Utilize CI/CD pipelines and APIs to deploy models and algorithms to target environments.
Work with cross-functional teams to understand needs and identify how data science can support operational, workforce planning, and business goals.
Perform analyses, visualize data, and present data science work to stakeholders.
Perform other duties as assigned.
Requirements
Bachelors or Masters degree in statistics, data science, or related field.
2-4+ years of demonstrated work experience in data science, machine learning, or statistics.
Strong theoretical and practical understanding of statistical analyses (e.g., hypothesis testing, regression, A/B testing), machine learning techniques (e.g., time series forecasting, supervised/unsupervised learning, etc.) and large language/AI models.
Proficient in Python (pandas, numpy, scikit-learn, etc.) and SQL for data extraction, modeling, preparation, analysis, and model building.
Experience in workforce optimization, workforce planning, or HR analytics.
Experience using data visualization software (e.g., Power BI, Tableau) for report creation and dashboard design.
Ability to write clear, maintainable, extensible, and testable code.
Experience with Git / GitHub.
Independent, organized, and solutions-driven.
Comfortable in a fast-paced environment that is subject to rapid change and innovation.