• Deliver automated document extraction and data enrichment solutions. • Manipulate data using Python or SQL Server; develop ad hoc queries to investigate data anomalies and to summarize data for pattern detection. • Manually label and validate small training data sets. • Develop round-trip data flows that prepare data for IDP and format the results for analytical purposes. • Collaborate with experienced modelers to build predictive models and analytic solutions using Python; apply techniques such as decision trees and clustering. • Under supervision use AI technologies such as natural language processing to extract business data from unstructured and semi-structured sources (e.g., loss history reports, insurance applications, claim files, application scraping using OCR/ generative AI, etc.). • Create technical documentation to archive end-to-end processes.

• Collaborate with team members to support translation of business problems into technical solutions • Complete assigned tasks in solution development for data extraction, classification, routing, or search using foundation models • Manipulate and analyze data programmatically following established patterns, and communicate findings clearly • Engineer foundation model prompts under guidance and evaluate using statistical metrics with support • Implement evaluation components using precision, recall, F-1 scores, and accuracy with guidance • Document work clearly with team review • Learn to identify component dependencies and contribute to problem decomposition

• The Lead Data Manager will be responsible for all data management activities in large, complex studies under general supervision • Primary communication point for project teams and company departments with regard to clinical data management • Coordination of data management activities within clinical projects related to EDC development, data collection, cleaning, reconciliation, Database Lock, development of final integrated database and generation of final Data Management deliverables • Primary responsibility for clinical data management activities within clinical projects • Study documents review • Data Management plan and other project-specific guidelines and instructions development and review • Co-ordinate/perform user acceptance testing • Development of Data Validation Plan/Edit Check Specifications • Data entry coordination and supervision • Data validation, including automatic and manual data inspections, data discrepancies/deficiencies review and resolution, query generation and resolution • Non-CRF electronic data handling • Database edits (paper CRF studies) • Manual CRF review (non-medical checks) • Clinical database lock • Coordination and supervision of processing, dispatch and archiving of CRFs/queries • Input into design of CRFs and other data collection forms; review Case Report Form design for accuracy and adherence to protocol • Development of CRF completion guidelines • Coding medical data with coding dictionaries and coding listings review • SAE reconciliation • Electronic data transfer specifications review and approval • Liaison with Document Center with regard to maintenance of TMF and eTMF • Database QC checks performance and documentation • Shared responsibility for preparation and attendance at internal and client/third-party audits/inspections of Data Management • Shared responsibility for follow-up on and resolution of audit findings pertaining to Data Management • Training of Assistant Data Managers • Project-specific training of investigators and clinical team in CRF completion

• Own the health, accuracy, and usability of data across our systems. • Manage and maintain data integrity across HubSpot and connected platforms, ensuring records are clean, deduplicated, and up to date. • Build and optimize HubSpot workflows, sequences, and automations to streamline operations and reduce manual processes. • Create and maintain dashboards, reports, and recurring data pulls for stakeholders across sales, marketing, and leadership. • Develop and enforce data governance standards, including naming conventions, property management, and lifecycle stage definitions. • Audit and troubleshoot data issues proactively—catch problems before they compound. • Own integrations between HubSpot and other tools in the stack (e.g., Zapier, Segment, spreadsheets, or internal databases). • Support ad hoc data requests and analysis for cross-functional teams, translating raw data into clear, actionable insights. • Document processes, SOPs, and system configurations so the team isn't reliant on tribal knowledge.