Job Description Summary
The Data, Analytics, AI & Platforms (DAP) Manager is the local technical anchor for data, analytics and AI within the Execution Excellence function at Novartis Pharma Switzerland. The role ensures Swiss commercial, Value & Access (V&A) and Medical Affairs teams get full value from the global Novartis analytics and AI ecosystem.
At its core, this is a Data & Analytics role with AI/GenAI and agentic AI as a strategic layer on top, supported by platform localization and operational readiness. The role works across the core No-vartis analytics stack; Python, Azure, Snowflake, and PowerBI, to deliver applied analytics, AI use cases and high-quality data products for the Swiss organization.
On the Data & Analytics side, the DAP Manager is hands-on and technically deep. Day to day, this means designing and maintaining local data models on Snowflake, building enterprise-grade PowerBI dashboards on top of them, leveraging Azure data and AI services for pipelines, model deployment and integration, and writing production-quality Python and SQL for applied data sci-ence, forecasting, segmentation, predictive modelling and automation. Local data flows into the global environment in close partnership with DDIT and CLS DAP. This is where the role is most hands-on and where its technical reputation is built.
On the AI/GenAI and agentic AI side, the role drives the local adoption and impact of AI prod-ucts aligned with the Novartis AI Strategy 2028, including AI-driven recommendations, Next Best Action, content optimization and emerging agentic AI workflows that autonomously plan and exe-cute multi-step analytical or operational tasks. The DAP Manager designs and prototypes applied AI use cases on Swiss data (often leveraging Azure OpenAI Service and the broader Azure AI stack) and contributes to the country AI roadmap. This is where the role creates disproportionate strategic value.
On the platform side, the role coordinates the localization and operational readiness of selected global marketing and customer-facing platforms (e.g., SFMC, CRM, NovartisPro and other global digital platforms) for the Swiss market. The platforms are designed, built and owned globally; the DAP Manager ensures they are correctly adapted, configured and operationally available locally, in partnership with the local Content Activation Managers, DDIT, BSI Hyderabad and external vendors.
Although this is an individual contributor role on paper, it sits within a technically strong EE team. The DAP Manager works shoulder-to-shoulder with the Integrated Insights Managers, who bring complementary analytical depth, and collaborates closely with International CLS DAP, BSI DAP Ops and global technical communities. This is not a lone-IC role: it is the senior technical anchor of a small, collaborative and technically credible team, with peers to think alongside on shared topics.
Indicative time allocation: ~40% Data & Analytics (hands-on technical core) / ~40% AI/GenAI and agentic AI (use cases, adoption, enablement) / ~10% Platforms (localization and operational coordination) / ~10% Cross-functional partnership and enablement.
Job Description
1. Data & Analytics
- Serve as a hands-on data and analytics practitioner in EE, writing production-quality Python and SQL for data manipulation, statistical modelling, machine learning and automation, in collaboration with technically strong Integrated Insights Manager peers.
- Design and maintain local data models, transformation logic and SQL pipelines on Snowflake, applying global data architecture standards to Swiss-specific needs in close partnership with DDIT and International CLS DAP.
- Leverage Azure data and AI services (e.g., Azure Data Factory, Azure ML, Azure Functions, Azure OpenAI Service) for data integration, model training and deployment, and AI workloads, in alignment with the global Novartis Azure environment.
- Ensure local data sources (IQVIA panels, OFAC, CRM activity, digital engagement, financial data, competitive intelligence) flow correctly into the global Snowflake environment and are fit for analytical use, with appropriate data quality, lineage, governance and Swiss data privacy compliance.
- Co-Lead the design, development and maintenance of all local performance dashboards and reports in PowerBI, covering commercial KPIs (HCP/product sales, market share, field metrics), V&A KPIs (access milestones, payer metrics), Medical sub-national dashboards and omnichannel/digital engagement metrics, with appropriate separation between commercial and medical views.
- Build robust, scalable PowerBI semantic models on Snowflake, ensuring dashboards are performant, refresh reliably and reflect a single source of truth for business-critical metrics.
- Develop and maintain standardized reporting packs (brand performance, launch tracking, field execution, campaign performance, ICE/IMEx reporting) in partnership with Integrated Insights Managers and functional owners.
- Independently design and execute applied data science projects, including regression, classification, clustering, time-series forecasting and uplift analyses, to assess sales dynamics, predict product uptake, support next-best-action decisions and optimize resource allocation.
- Ensure local governance, quality and enablement of performance management tools such as BEST and C360, maintaining alignment with International reporting frameworks.
- Build reusable analytical code, templates and automation workflows that increase efficiency and reproducibility across the team.
- Translate analytical outputs into clear, actionable recommendations in partnership with the Integrated Insights team.
2. AI & Agentic AI
- Drive local adoption and impact of AI/GenAI and agentic AI products aligned with International DAP strategy and the Novartis AI Strategy 2028, including AI-driven recommendations, Next Best Action, execution orchestration tools, content and campaign optimization, and emerging agentic AI workflows that autonomously plan and execute multi-step analytical or operational tasks.
- Design and prototype applied AI/GenAI and agentic use cases on Swiss data, leveraging Azure OpenAI Service, Azure ML and the broader Azure AI stack where appropriate, in close partnership with Integrated Insights Managers and functional stakeholders, focusing on use cases that deliver measurable business value.
- Collect and prioritize local business requirements for AI/GenAI, agentic AI and advanced analytics; co-define the country AI roadmap with the Head Integrated Insights & DAP, International CLS DAP and BSI DAP Ops.
- Define and apply measurement frameworks for AI use cases (adoption, recommendation quality, time saved, autonomy levels for agentic solutions, business impact on key KPIs) and consolidate learnings to inform future roadmap decisions.
- Champion responsible and ethical AI use locally, ensuring all AI/GenAI and agentic deployments respect data privacy, the Novartis Code of Ethics, functional separation requirements (including medical/commercial boundaries) and emerging AI governance standards.
- Coordinate with Capability Building to plan and deliver AI/GenAI and agentic AI training and enablement, ensuring users understand how to interpret, oversee and safely act on AI-generated insights and AI agent outputs.
3. Platforms
- Coordinate the localization, configuration and operational readiness of selected global marketing and customer-facing platforms (e.g., SFMC, CRM, NovartisPro and other global digital platforms) for the Swiss market, in alignment with International CLS DAP, IMA IMACE DAP and V&A guidance.
- Together with the local Customer Excellence team, ensure day-to-day platform availability, performance and incident resolution in partnership with BSI Hyderabad, DDIT and external vendors, maintaining business continuity for campaigns, field execution and medical engagement activities.
- Together with the local Field Excellence team, configure CRM platforms appropriately for Commercial, V&A and Medical Affairs use cases, ensuring compliance with data access, privacy and medical/commercial separation requirements.
- Support IMEx tech readiness (including MEEP) by ensuring platforms, integrations, roles and permissions are in place ahead of IMEx milestones.
4. Cross-Functional Partnership & Enablement
- Partner closely with the Integrated Insights Managers as a technical peer, providing the data infrastructure, dashboards, analytical products and AI capabilities they need to deliver insight, while drawing on their analytical depth on shared topics. Together, the DAP Manager and Integrated Insights Managers form the collaborative technical core of EE.
- Partner with Integrated Insights on forecasting, scenario planning and market model consolidation by providing robust performance data, KPI trends and AI-assisted analytical views.
- Provide V&A-specific and Medical-specific analytics enablement (e.g., access KPI dashboards, Medical sub-national metrics, iSEC/VIP-based analytics) while keeping strategy and decision-making with V&A and Medical Affairs, and within medical/commercial boundaries.
- Together with Integrated Insights Managers, promote data literacy, analytical self-service and AI fluency across the organization, enabling stakeholders to extract value from dashboards, data tools and AI capabilities independently.
- Together with Integrated Insights Managers, document data models, dashboard logic, AI use case designs and platform configurations to ensure knowledge continuity and efficient onboarding.
- Support International reporting requirements related to ICE/IMEx and ensure alignment with International DAP frameworks on all analytics, AI and platform deliverables.
Skills Desired
Artificial Intelligence (AI), Biostatistics, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Logistic Regression Model, Machine Learning (ML), Machine Learning Algorithms, Nlp (Neuro-Linguistic Programming) And Genai, Pandas (Python), Python (Programming Language), R (Programming Language), Sql (Structured Query Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis