OMG Tech Partners

• Lead projects related to data warehousing, data pipelines, and data products. • Manage project timelines, capacity and demand planning, and financial reporting. • Collaborate with stakeholders to ensure project objectives are met. • Oversee reporting and monitoring of project progress and deliverables. • Communicate effectively with technical and non technical teams.

• Analyze, interpret, and transform data to actionable insights using SQL, Python, and BI tools like Power BI. • Develop KPIs and metrics based on business rules. • Ensure data governance, lineage, metadata management, parameterization, and version control. • Collaborate with product, engineering, and architecture teams to support data-driven decisions. • Apply hypothesis testing, data quality analysis, and statistical techniques to solve complex problems. • Work effectively in Agile delivery environments and navigate ambiguity. • Support compliance with regulated environments (GxP, ISO) and validation requirements.

• Opportunity discovery & prioritization: Run process discovery with supply chain teams (planning, scheduling, inventory, procurement, logistics) to map and size AI opportunities; build a sequenced backlog tied to measurable outcomes. • Hands-on POCs to MVPs: Rapidly build POCs and evolve the best into MVPs using AWS + Databricks + Dataiku + OpenAI, integrating with enterprise data sources and real workflows. • Roadmap ownership: Produce and maintain a capability roadmap (90 days/6 months/12 months) with dependencies, data readiness needs, and adoption/change requirements. • Kinaxis-enabled use cases: Design solutions that complement and enhance Kinaxis workflows (exception management, scenario narratives, decision support, insight generation, planning explainability). • Balance quick wins vs foundations: Deliver short cycle wins that work today while shaping requirements for scalable infrastructure as IT builds out data/AI foundations (reusable components, standard integration patterns, agent orchestration approaches, monitoring/evaluation patterns). • Vision and transition plan: Define a north-star vision and pragmatic transition plan bridging today's constraints to the medium/long-term target state (platform, governance, integration patterns, reusable AI assets). • Production-minded engineering: Define architecture, evaluation metrics, guardrails, monitoring, and last-mile integration patterns; partner with platform/data teams while remaining personally hands-on. • Drive ChatGPT adoption: Build role-based prompt libraries, playbooks, templates, and enablement sessions; measure adoption and iterate based on usage and impact. • Stay current and apply responsibly: Maintain working knowledge of the rapidly evolving AI landscape (GenAI, agents, evaluation, toolchains) and translate relevant advances into practical solutions aligned to enterprise guardrails.

• Map DPD opportunities for AI: Identify and size use cases across drug product operations (execution insights, investigations support, batch record intelligence, documentation acceleration, knowledge retrieval, exception management, production performance). • Build and ship solutions: Deliver POCs/MVPs hands-on - integrating GenAI with operational data and controlled documentation in secure, controlled, and adoption-ready ways. • Roadmap and value cases: Own a DPD AI roadmap with value sizing, dependencies, and adoption plans; align priorities with DPD stakeholders. • Balance quick wins vs foundations: Build what is needed now while shaping and standardizing patterns that will scale with evolving enterprise data/AI infrastructure (reusable components, integration standards, evaluation/monitoring, agent orchestration patterns). • Vision and transition plan: Define the DPD AI north star and a staged plan from POCs to reusable capabilities to scaled solutions as infrastructure matures. • Quality-aware design: Build with regulated realities in mind (traceability expectations, controlled content boundaries, auditability of outputs where required). • Drive ChatGPT adoption: Create role-based prompt kits, playbooks, SOP-adjacent guidance, and enablement sessions; measure adoption and iterate. • Stay current and apply responsibly: Continuously monitor the rapidly advancing AI landscape and selectively bring in relevant capabilities that are safe, practical, and measurable in DPD operations.

• Identify high-leverage DSD use cases: Target opportunities across DS operations (technical knowledge retrieval, investigation support, documentation acceleration, operational insights, exception triage, capacity/coordination decision support). • Build prototypes and deliver MVPs: Create working solutions using AWS/Databricks/Dataiku/OpenAI, integrating operational data and documents into usable workflows. • Data readiness and integration: Assess data availability/quality; define what's needed and influence how to get there (standard sources, access patterns, reusable pipelines). • Roadmap ownership: Produce a DSD capability roadmap with milestones, dependencies, and impact metrics. • Balance quick wins vs foundations: Deliver what helps DSD now while shaping patterns that will scale as the enterprise platform matures (reusable assets, integration standards, evaluation/monitoring, agent orchestration approaches). • Vision and transition plan: Define the DSD AI north star and a staged path from POCs to reusable capabilities to scaled delivery. • Drive ChatGPT adoption: Build prompt libraries, playbooks, and training tied to real DS work; measure usage and outcomes. • Stay current and apply responsibly: Keep pace with AI advances and translate them into practical, controlled, high-impact solutions suitable for DSD environments.

• Map CTD AI opportunities: Identify use cases across CTD workflows (knowledge retrieval, document acceleration, exception management support, operational insights, coordination/handoff support, client-response acceleration). • Build and ship: Deliver POCs/MVPs using AWS/Databricks/Dataiku/OpenAI, integrating with operational systems, data, and document repositories. • Roadmap and operating cadence: Own a roadmap and drive an execution cadence (demos, stakeholder reviews, adoption checkpoints, impact tracking). • Balance quick wins vs foundations: Deliver immediate workflow improvements while shaping requirements and reusable patterns that will scale as IT infrastructure matures (integration patterns, evaluation/monitoring, orchestration approaches, governance-aligned design). • Vision and transition plan: Define the CTD AI north star and a staged path from POCs to reusable capabilities to scaled delivery. • Adoption-first delivery: Convert solutions into practical assets - templates, prompt kits, role-based guides, and training - so adoption is built in. • Risk/controls awareness: Incorporate appropriate controls for privacy/security and traceability expectations where needed. • Stay current and apply responsibly: Keep up with the rapidly advancing AI landscape and selectively introduce relevant techniques/tooling that are safe, measurable, and adoptable in CTD operations.

• Providing technical expertise to develop reports in a CoStar • Implementation of report suite • Developing customized reports for specific business needs • Updating reports as necessary to ensure accuracy • Draft Standard Operating Procedures (SOPs) for new processes and report management • Ability to troubleshoot and resolve technical issues related to CoStar • Create or setup new transaction types within the application.

• Design, host, manage, and support AWS-based AI and application infrastructure in production environments. • Architect and manage MCP Servers hosted on AWS, ensuring scalability, availability, and security. • Deploy and manage containerized workloads using Docker on ECS, EKS, and Fargate. • Support and administer core AWS infrastructure including EC2, networking, and security configurations. • Work with AWS AI services, including Amazon Bedrock and Amazon SageMaker, to support AI/ML workloads. • Manage and support AWS databases (both managed and self-hosted) for application and AI use cases. • Implement and maintain DevOps practices, CI/CD pipelines, and infrastructure automation in AWS. • Configure and manage API Gateway, including secure access, integrations, and authorization. • Ensure AWS environments follow best practices for security, IAM roles and policies, scalability, and reliability. • Monitor, troubleshoot, and resolve production issues across AWS platforms. • Provide AWS platform administration and ongoing support. • Document system architectures, deployment processes, runbooks, and operational procedures.

• Design, develop, and maintain software applications. • Work across the full stack – front-end and back-end development. • Collaborate with stakeholders to translate requirements into technical solutions. • Provide technical leadership and guidance to junior developers. • Ensure compliance with standards in regulated environments.