IT Services for Businesses

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 Help Medical Affairs teams turn strategy into measurable execution—with clear KPIs, operating rhythm, and leadership-ready insight.

• Medical Affairs metrics evaluation — define and track KPIs across Field Medical, MedInfo, insights, events, and quality proxies.
• Field / MSL metrics evaluation — scientific exchange, KOL coverage, white space, insight capture, and coaching signals.
• Strategy implementation — translate medical strategy into field plans, milestones, owners, and review cadence.
• Medical strategy & execution — close the loop from plan → activity → insight → decision.
• Competitive intelligence — structured CI capture, thematic synthesis, and briefing packs.
• Congress planning — pre-/during-/post-congress plans with insight capture and follow-up accountability.
• Medical excellence — standards, playbooks, and consistent quality across HQ and field.
• Content generation & development — modular, decision-ready content for leadership and field use.
• Survey & data collection / reporting — design, collect, analyze, and report with clear stakeholder outputs.
• MLR support — review-ready content workflows and audit-friendly packaging

Modernize SAS analytics into enterprise R and Python with agentic AI that speeds translation, validation, and team enablement—without sacrificing quality, control, or auditability.

• Design, develop, deploy, maintain, and train teams on R- and Python-based AI-enabled data applications.
• Build GenAI tools that support SAS→R migration and hands-on analyst training.
• Automate code translation, conversion, correction, and validation pipelines.
• Support IT/admin teams in standing up R infrastructure suitable for regulated and CRAN-aligned workflows.
• Script SOPs and guidelines so migration and delivery stay consistent across projects.
• Prototype and package modern reporting apps in R Shiny for stakeholder review and production rollout.

Design, build, and operate production-grade Shiny applications—from prototype to secure enterprise deployment—with strong UI, modular code, and reliable delivery practices.

• Support open-source application development and the infrastructure needed to run it well.
• Design, develop, deploy, and maintain enterprise Shiny applications end to end.
• Customize and integrate UI widgets to improve interaction, clarity, and usability.
• Build modular R functions and packages for cleaner data wrangling and reuse.
• Strengthen automated testing to improve quality and release confidence.
• Package, deploy, and implement Shiny apps across local, server, and cloud environments.

Upskill teams to work confidently in open-source R and Python—including analytics for CDISC standards and Real-World Evidence (RWE)—with cleaner code, stronger collaboration, and production-ready practices.

• Train SAS and traditional analytics teams to adopt R and Python workflows.
• Teach clean, efficient, well-documented coding in R and Python.
• Cover CDISC-aligned data and analysis practices in R (SDTM/ADaM concepts, reproducible reporting, submission-ready habits).
• Build capability for RWE analysis in R/Python: real-world data handling, cohort logic, outcomes analysis, and transparent reporting.
• Strengthen collaboration on cross-functional open-source projects.
• Practice troubleshooting, code review, and end-user support across the open-source stack.

Build and improve geospatial applications that turn location data into clear maps, interfaces, and decisions—using ESRI, JavaScript, R, and Python.

• Develop, maintain, and enhance GIS applications with ESRI technologies.
• Write clean, efficient, well-documented code in JavaScript, R, and Python.
• Partner with cross-functional teams to design and deliver geospatial solutions.
• Troubleshoot and resolve issues in existing GIS apps and map interfaces.
• Stay current with ESRI capabilities and geospatial best practices.
• Participate in code reviews and continuously improve delivery processes.
• Provide technical support and guidance to end users as needed.

Protect analytics platforms, applications, and data pipelines with practical security controls built for modern R/Python delivery.

• Secure enterprise data apps and APIs across development, test, and production.
• Harden access, authentication, and environment controls for analytics workloads.
• Support network and endpoint protections where they matter to app and data security.
• Run security reviews and audits focused on code, packages, secrets, and deployment posture.
• Help teams adopt secure-by-default practices for open-source analytics stacks.

Align technology choices with business goals by building a clear roadmap for analytics modernization—SAS→R/Python, open-source platforms, and sustainable delivery.

• Assess current analytics stack, skills, tooling, and delivery bottlenecks.
• Define roadmaps for SAS→R/Python migration and open-source adoption.
• Prioritize use cases where R, Python, and Shiny create measurable value.
• Guide platform decisions (environments, packaging, governance, and cloud).
• Support digital transformation with practical operating models and capability plans.
• Help leadership sequence investment: quick wins, foundational platforms, then scale.

Host and operate analytics applications in the cloud so teams can deliver faster, scale cleanly, and stay reliable.

• Deploy and host R Shiny and related data applications in cloud environments.
• Set up cloud storage and data access patterns that support analytics workflows.
• Build and modernize cloud-ready application architectures for R/Python services.
• Support CI/CD-friendly packaging and release paths for analytics apps.
• Optimize cloud setups for performance, cost, and operational stability.