Data Science Engineer

nimble Solutions is seeking a Data Science Engineer to build and operationalize data products that improve healthcare revenue cycle performance. This role will work with data from the company’s warehouse and business applications to develop trusted analytics, predictive models, and decision-support tools for RCM teams. The successful candidate combines strong data engineering skills with applied data science expertise and can translate operational questions into reliable, measurable solutions.

Microsoft Fabric engineering experience is a must. Most data for this role will come from nimble’s new data warehouse, so the engineer must be able to work effectively with Fabric-based data pipelines, lakehouse/warehouse patterns, transformations, and analytics workflows.

Key Responsibilities

• Build, maintain, and optimize data ingestion and transformation pipelines in Microsoft Fabric.
• Develop and support lakehouse and warehouse data structures, curated data models, and reusable datasets for analytics and data science.
• Integrate data from the enterprise warehouse and relevant RCM, financial, operational, and third-party sources, subject to approved access and data governance.
• Profile, validate, reconcile, and document data; investigate quality issues and work with source systems and business teams to resolve them.
• Apply statistical analysis and machine-learning methods to practical RCM problems, such as denial patterns, accounts receivable prioritization, payment and claims trends, workflow forecasting, and operational capacity planning.
• Develop reproducible feature engineering, model training, evaluation, and scoring workflows; select metrics that reflect business outcomes and model risk.
• Deploy, monitor, and maintain data products and models in collaboration with data engineering, analytics, applications, and business teams.
• Create clear visualizations, dashboards, and written findings that help RCM stakeholders understand performance, drivers, and recommended actions.
• Partner with subject-matter experts to define use cases, success measures, requirements, and appropriate human review for decision-support outputs.
• Follow data governance, privacy, security, and responsible AI practices, especially when working with sensitive healthcare or financial information.
• Maintain technical documentation, lineage, testing, and support procedures for pipelines, datasets, and models.