Role Description As a Data Scientist on our Applied Research team, you will build the intelligent systems that create the data our customers depend on. You will: - Design extraction and classification models that process enterprise-scale document corpora. - Build and evolve the entity resolution and signal detection layers powering the Commercial Graph and Financial Graph. - Define how AI capabilities surface as recommendations, agents, and search across the platform. - Own the models, pipelines, and graph structures that are the product. - Work directly with engineering, product, and customers on problems where a single clause can represent tens of millions of dollars of exposure. - Ensure model accuracy has a contractual SLA. Qualifications - 5+ years of experience in data science, applied ML, or AI research with production-shipped systems, not just notebooks and prototypes. - Strong statistical foundations and the ability to define and evaluate success metrics for AI systems including precision, recall, coverage, latency, not just accuracy. - Deep experience building NLP, NLU, or document understanding models that operate on messy, real-world unstructured data at scale. - Strong intuition for entity resolution, knowledge graph construction, or graph-based modeling. - Hands-on proficiency in Python and modern AI frameworks, with experience deploying models into production pipelines. - Comfort with information extraction, classification, and retrieval-augmented generation patterns applied to real enterprise workloads. - A track record of working cross-functionally with engineering and product to shape what gets built. - Clear, structured communication skills. - High ownership mentality regarding model quality, pipeline reliability, and customer outcomes. Requirements - Experience building or evolving knowledge graphs, commercial ontologies, or financial data models in enterprise contexts. - Prior work on document AI, OCR pipelines, or hybrid extraction systems combining rule-based and learned approaches. - Exposure to AI agent architectures, tool-use patterns, or autonomous reasoning systems in production. - Background in procurement, contract management, spend analytics, or financial operations domains. - Experience with evaluation frameworks for AI systems (RAGAS, custom eval harnesses, human-in-the-loop QA pipelines). - Familiarity with distributed data platforms, event-driven architectures, or streaming systems (Ray, Kafka, Azure Service Bus). - Prior work at a high-growth startup or enterprise AI company. - An MS or PhD in a quantitative field. Benefits - Competitive salary. - Annual performance bonus. - Employee stock option plan. - 100% paid medical, dental, and vision coverage. - 401(k) with employer contribution. - Generous vacation and sick leave. - Flexible work arrangements. - High-quality equipment for home and office. - Strong culture of collaboration, mentorship, and continuous improvement.

• Analyze permit submission comments and rejection patterns to identify the most common causes of delays and opportunities to improve review cycle times. • Build, maintain, and refine NLP pipelines that classify reviewer comments into meaningful topics using clustering techniques and LLM-based approaches. • Design, test, and optimize prompts for Azure OpenAI and other AI models to classify, summarize, and extract insights from unstructured text. • Develop metrics, dashboards, and reports that translate data analysis into actionable recommendations for internal and external stakeholders. • Write and optimize SQL queries and Python-based ETL processes to extract, transform, and load data from SQL Server and cloud-based storage environments. • Evaluate AI and machine learning model outputs for quality, accuracy, consistency, and appropriate handling of sensitive information. • Design and execute experiments to measure and improve the effectiveness of classification models, prompts, and analytical approaches. • Partner with cross-functional teams to support data-driven decision-making and continuous improvement initiatives.

• Responsible and manages all aspects of the clinical trial data management process from study start up to post database lock for assigned projects • Primary Data Management (DM) contact (Lead DM role) for assigned clinical project(s) / program(s) • Oversee project data entry process including development of data entry guidelines, training, data entry quality and resourcing • May perform quality control of data entry • Provide input, assess and manage timelines • May develop CRF specifications from the clinical study protocol • Conduct database build UAT and maintain quality controlled database build documentation • Responsible for creating, revising, appropriate versioning and maintaining data management documentation • Train clinical research personnel on the study specific CRF, EDC, and other project related items as needed • Review and query clinical trial data according to the Data Management Plan • Perform line listing data review based on the guidance provided by the sponsor and/or Lead DM • Run patient and study level status and metric reporting • Perform medical coding of medical terms relative to each other in order to ensure medical logic and consistency • Coordinate SAE/AE reconciliation • Liaise with third-party vendors such as external data and EDC vendors • Identify and troubleshoot operational problems, issues, obstacles, and barriers for studies • Participate in the development and maintenance of Standard Operating Procedures (SOPs) and corresponding process documentation related to data management and database management activities • Communicate with study sponsors, vendors and project teams as needed regarding data, database or other relevant project issues

• The Clinical Data Manager II/Senior Clinical Data Manager is responsible and manages all aspects of the clinical trial data management process from study start up to post database lock for assigned projects. • Supervise project data entry process including development of data entry guidelines, training, data entry quality, and resourcing. • Ensure clinical data management deadlines are met with quality. • Conduct database build UAT and maintain quality controlled database build documentation. • Provide input, assess and manage timelines. • Review and query clinical trial data according to the Data Management Plan. • Identify and troubleshoot operational problems, issues, obstacles, and barriers for studies based on metrics data, audit reports, and input from project team members and other stakeholders.