Clinical Informaticist

Wolters Kluwer•Madison, WI

1d•Hybrid

About The Position

The Clinical Informaticist combines clinical expertise, healthcare informatics, and data science to support the development, implementation, and optimization of Wolters Kluwer Health solutions. This role applies knowledge of clinical workflows, healthcare data, and surveillance practices to ensure products deliver clinically meaningful insights that improve patient safety, medication stewardship, and operational outcomes. The Clinical Informaticist partners with healthcare organizations to understand clinical and operational workflows, translate customer requirements into effective surveillance strategies, and facilitate successful adoption of the Sentri7 Drug Diversion platform. Working closely with product, engineering, data science, and customer-facing teams, this role helps align technology solutions with real-world healthcare practice.

Requirements

Bachelor's, Master's, or doctoral degree in a clinical, informatics, or quantitative field — e.g., pharmacy (PharmD/RPh), nursing (RN/BSN/MSN), medicine, health/clinical informatics, data science, or a related discipline; Equivalent real-world experience — demonstrated, hands-on work in clinical operations, healthcare data, clinical surveillance/decision support, or applied data science/AI that provides equivalent knowledge and capability, in lieu of a specific degree.
Relevant certifications, clinical licensure (e.g., PharmD, RN, RPh, CPhT), portfolio work, or a proven track record of shipping data/AI solutions in healthcare can substitute for formal degree requirements.
Clinical background — pharmacy, nursing, or health informatics (PharmD, RN, RPh, MD, or equivalent) — with a strong understanding of medication-use and clinical workflows.
Data science proficiency: Python and/or R, SQL, statistical modeling, and ML.
Experience taking a model or analysis from data to validated results.
Strong grasp of healthcare data standards and medication-identity references (RxNorm, NDC, SNOMED CT, ICD, LOINC, GPI, MME).
Experience with clinical surveillance, medication safety, diversion monitoring, or clinical decision support software.
Experience profiling and validating large, messy real-world clinical datasets (relational SQL; Oracle a plus).
Familiarity with EMR (Epic/Meditech/Cerner) and ADC (Pyxis/Omnicell) data, HL7/FHIR, and interoperability.
Rigorous, hypothesis-driven analytical approach; ability to eliminate benign explanations and reach evidence-based, defensible conclusions.
Responsible handling of PHI and strong communication for both technical and clinical audiences.
4+ years of hands-on professional experience in data science, predictive modeling, or machine learning.
Experience with acute care hospital data systems and care delivery workflows
Scripting/automation for data validation (PowerShell/Python); MLOps and model-monitoring familiarity is a plus.

Nice To Haves

Advanced degree in a clinical, informatics, or quantitative discipline, and/or equivalent depth of applied experience preferred.

Responsibilities

Customer engagement to understand continuous infusion workflows in the outpatient, inpatient, and procedural setting.
Review of customer data to identify any gaps in documentation or in process that would ultimately impact the successful use of the application.
Enable system configurations, including mappings and parameter changes, that support customer workflows.
Profile, map, and validate real-world healthcare data (medication, clinical, surveillance, terminology), so product output is clinically accurate, complete, and interoperable.
Support customer configuration, onboarding, and diversion/surveillance investigations; distinguish product defects from data/configuration issues, always validating deployed behavior against production ground truth.
Complete validation testing prior to customer validation.
Provide ongoing support during the implementation process by providing regular status updates and soliciting customer feedback.
Analyze customer assessment notes in the UI to identify any gaps or areas needing improvement.
Collaborate with the customer support team to provide post-live customer support to understand ongoing issues to efficiently and effectively resolve support tickets.
Partner with Customer Success Manager (CSM) to offer personalized, high-touch support to customers requiring additional assistance to promptly and effectively address specific needs and concerns.
Communicate progress and outcomes to customers and project managers regularly.
Offer constructive feedback and recommendations based on customer input.
Ensure customers feel heard and valued through transparent communication.
Build synthetic/de-identified datasets that preserve clinical integrity (age-, sex-, route-, dose-appropriate) for demo, testing, and model development — without exposing real PHI.
Normalize and standardize clinical data using terminology/code systems (RxNorm, NDC, SNOMED CT, ICD, LOINC, GPI) to produce AI-ready datasets.
Partner with data engineering on ETL pipelines (e.g., multi-tenant Oracle, contract/point-in-time model, master processing, reporting) and with product/clinical teams to translate findings into features and clinician-facing documentation.
Communicate results to clinical and non-technical stakeholders through clear analyses, validation reports, dataflow/ERD documentation, and plain-language release notes.