Minimum Qualifications:
- Minimum three (3) years experience working with Exploratory Data Analysis (EDA) and visualization methods.
- Minimum three (3) years machine learning and/or algorithmic experience.
- Minimum three (3) years statistical analysis and modeling experience.
- Minimum three (3) years programming experience.
- Minimum one (1) year experience in a leadership role with or without direct reports.
- Bachelors degree in Mathematics, Statistics, Computer Science, Engineering, Economics, Public Health, or related field AND Minimum five (5) years experience in data science or a directly related field. Additional equivalent work experience in a directly related field may be substituted for the degree requirement. Advanced degrees may be substituted for the work experience requirements.
Overview:
The Inventory Control Tower Business Intelligence Engineer role is a specialized function responsible for enabling enterprise-wide inventory visibility, standardization, and optimization across the health system.
This role designs, builds, and operates the data, models, and logic that power the Inventory Control Tower, with a direct focus on inventory management execution, leveraging emerging technologies, business intelligence and AI/machine learning. Key responsibilities include establishing and maintaining inventory management methodologies (e.g., min/max, replenishment logic, inventory positioning), enabling real-time visibility into inventory conditions, and supporting proactive management of supply risk and utilization.
Working at the intersection of supply chain operations and advanced data capabilities, this individual translates complex, multi-source inventory data into actionable operational insights that drive decision-making, improve service levels, and reduce waste. The role is accountable for embedding consistent, data-driven inventory practices into core supply chain workflows, enabling scalable and measurable control of inventory across clinical and non-clinical environments.
Job Summary:
This individual contributor is primarily responsible for designing and developing data pipelines and automation for data acquisition and ingestion of raw data from multiple data sources and data formats by transforming, cleansing, and storing data for consumption. This role is also responsible for developing detailed problem statements outlining hypotheses and their effect on target clients/customers, analyzing and investigating complex data sets and summarizing key characteristics, selecting, manipulating and transforming data into features used in machine learning algorithms, training statistical models, deploying and maintaining reliable and efficient models through product