Data Scientist

• AI / ML Python development using frameworks like LangGraph, LangChain, etc.
• Data science development using Python notebooks locally and in SAP Databricks
• Embed AI solutions into the SAP eco-system using SAP Joule Studio
• Build and deploy Python applications in SAP Business AI Platform - Cloud Foundry environment using frameworks like Flask, etc.
• Collaborate closely with the SAP Lead Architects, Functional Specialists and analytics / data science team to:
• Build AI / ML solutions within the SAP eco-system of business applications
• Build advanced pipelines for data science and analytical solutions
• Collaborate with the developer team to support existing solutions

AI and Data Science Developer, SAP Development

• Lead and guide the integration team in the design and delivery of integration solutions
• Define integration strategies and select appropriate patterns for connecting the MDM platform with third-party systems
• Plan, structure, and coordinate all integration activities, including effort estimation and resource planning
• Develop and maintain detailed integration workplans aligned with architecture and program governance
• Oversee end-to-end execution of integration deliverables, ensuring quality, consistency, and timely delivery
• Collaborate closely with architects, business stakeholders, system experts, and project management
• Manage dependencies and alignment with parallel initiatives and enterprise programs
• Identify risks and issues early and drive mitigation actions
• Ensure integration solutions follow best practices in scalability, performance, security, and maintainability

Data Integration Lead

• Merge and standardize datasets from multiple sources.
• Perform data profiling and quality analysis using SQL/DAX.
• Develop predictive models and optimize algorithms.
• Create reports and communicate insights to stakeholders and client.
• Identify patterns and recommend data-driven actions.

• 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), ensuring that there is back-up, continuity, responsiveness, and that tasks are performed in a timely manner.
• Oversee project data entry process including development of data entry guidelines, training, data entry quality and resourcing
• Provide input, assesses and manage timelines. Ensure that clinical data management deadlines are met with quality.
• Conduct database build UAT and maintain quality controlled database build documentation. Oversee overall quality of the clinical database.
• Responsible for creating, revising, appropriate versioning and maintaining data management documentation. Oversee completeness of data management documentation for the Trial Master File.
• 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.
• Liaises with third-party vendors such as external data and EDC vendors in a project manager capacity in support of timelines and data-related deliverables.
• 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.

Clinical Data Manager II – Senior Clinical Data Manager

• 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), ensuring that there is back-up, continuity, responsiveness, and that tasks are performed in a timely manner.
• 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. Ensure clinical data management deadlines are met with quality.
• Conduct database build UAT and maintain quality controlled database build documentation.
• Responsible for creating, revising, versioning and maintaining data management documentation. Oversee completeness of data management documentation for the Trial Master File.
• 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.
• Coordinate SAE/AE reconciliation.
• Liaise with third-party vendors such as external data and EDC vendors in a project manager capacity in support of timelines and data-related deliverables.
• Identify and troubleshoot operational problems, issues, obstacles, and barriers for studies based on metrics data, audit reports, and input from project team members.

Senior Clinical Data Manager II

• Design, build, and ship production retrieval systems that power AI product capabilities across multiple products
• Own vector store selection and operation, including scalability, latency, reliability, cost, and multi-tenant design
• Build and operate indexing and refresh pipelines: chunking, embedding generation, backfills, deletes, and versioned indices
• Implement semantic and hybrid retrieval: embeddings, similarity search, lexical and vector combination, metadata filtering, and reranking
• Own retrieval evaluation as a first-class system: gold sets, offline relevance metrics, slice analysis, drift detection, and regression gates that block bad changes from shipping
• Make and defend the core tradeoffs of the domain: relevance against latency against cost against operational complexity
• Partner with AI Platform and infrastructure engineers on deployment, observability, and reliability, and with product and research partners on relevance requirements

Senior Applied Data Scientist, Retrieval and Semantic Systems

• Architect, build, and operate scalable inference services, APIs, and backend components for model-driven and LLM-powered product features
• Productionize AI and ML workflows with strong MLOps practices: model versioning, testing, deployment pipelines, monitoring, rollback, and operational reliability
• Define and implement evaluation frameworks for model quality, system reliability, latency, and cost, and make these a standard part of how models ship
• Build reusable platform patterns, service templates, and reference implementations that multiple teams and products can adopt
• Set and uphold engineering standards across the AI team: code quality, documentation, observability, and incident readiness, and mentor team members in production ML practices
• Partner with our infrastructure owner on the underlying cloud, cluster, and CI substrate, and with product, engineering, and research partners to move AI capabilities into production

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