Strategic purpose of this role is to organize Novartis data, make it easily accessible, and useful to authorized roles. This role will help drive the development and execution of Novatis’ ambition to turn data into a real strategic asset across the organization. This ambition is one of key pillars in the broader digital transformation happening at Novartis to be a ‘medicines and data science company.’ This data centric role will facilitate using data to digitize the biopharma value chain in finding right target, right tissue, right safety, right patients, right commercial potential and to optimize omni channel stakeholder experience and engagement.
More specifically, the purpose of this role is to engineer, in partnership with over 10+ business units and Technology partners (internal and external), global adoption of consistent Data Life Cycle (DLC) management framework, processes, architecture and supporting tools. Key areas in the Data Life Cycle management includes Master Data Management, Data Governance, Information Modeling, and Data Science Enablement. This role will specifically focus on improving maturity of Data Ownership and Capability Building in all bio pharma data domains across the value chain; omics, compounds, diseases, patients, payers, providers, sites, trials, KOLs, HCPs, EMR, EHR, patient journeys, employees, contracts, vendors, products, etc
This role will directly report and work with the Head of Data Strategy in the Group Digital Office.
• Exercises clear and informed decision-making using process transparent to stakeholders for data related investment and creation of Data Ownership and Capability Building for all units and functions. Keeping in touch with every stakeholder regularly. Facilitating meetings for the senior stakeholders & teams (preparing, moderation and post processing). Holding retrospectives to refine approach.
• Helps review project deliverables & plans, monitors project progress, drives enterprise adoption, communicates policies and standards, approval of standards and definitions. Helping to write or split user stories. Helping to write or adapt product visions. Helping to order product backlog items. Helping with the release planning.
• Escalation point for data related product and service owner & working groups (master data management, information architecture, and data science enablement). Coaching team members (e.g. with one-on-one coaching). Mediating through conflicts. Helping the team to make decisions. Fostering the team self-organization. Mediating the general conflict of goals between teams (technical quality) and product owner (more features).
• Champions and builds awareness and value of Data Ownership and Capability Building capabilities. Assess maturity. Organizing exchange events like Open Spaces or World Cafés for the team, its stakeholders, and its organization.
• Helps BUs create targeted roadmaps and drives adherence to standards when creating new solutions. Exchanging constantly with BU teams in the organization (e.g. through community of practice). Helping the team to get rid of impediments. Suggesting new metrics for the team as catalysts for change.
• Drives organic adoption of the capabilities, helps conducts investment planning by utilizing common Data Ownership and Capability Building Capabilities. Sharing insights throughout the company (micro-blogging, blogging, internal conferences, etc.).
• Aligns appropriate BU capabilities to Enterprise-level efforts. Drives continuous feedback in product management and svc ownership
• Refine the Novartis reference Data Ownership and Capability Building model and keep it update. Develop service catalog in collaboration functions and ensure alignment of projects to avoid gaps or duplication of effort. Drive key projects (lighthouses) towards using common DLC services.
• Asking open questions.
Drive strategy and implementation of product and services for data engineering with embedded Ownership
• Drive strategy and implementation of products & services for data Discovery, Capture, Store, Quality, Profile, Metadata/Glossary, Curate, Classify, Lineage, Standardize, Normalize, De-Dup, etc phases of Data Life Cycle management. Develop and maintain Novartis Data Ownership and Capability Building model / process and ensure alignment and adoption across Novartis.
• Ensure compliance with FAIR principles to maximize interoperability.
• Ensure availability of data catalogs with searchable metadata and automated validation of data assets against interoperability standards.
• For internal/external master data and transactional data sources, define programmatic services to govern against identifier uniqueness, identifier persistence, machine-readability of metadata, URI in metadata, indexing and searching (meta)data. Define services for access protocol using open standards, vocabularies, MDM standards, access authorization, metadata longevity etc.
• Define taxonomies to organize data into managed data lake, raw zones, conformed zone, analytical zone using relational, NoSQL, data lakes, warehouses/marts, graph DBs, etc.
Promoting cultural change
• Drive development and implementation of a change management concept in close collaboration with HR and closely aligned with the overall Novartis digital transformation to build a more data-centered mindset, incl., capability building, change agents, talent placement and more inspired, empowered, unbureaucratic organization.
• Create a curious, continuously learning (from successes and failures) environment.
• Develop operating model to make the business truly own and appreciate the value of the data. Define metric to measure success on becoming a more data-centric organization.
• Develop an environment which fosters a high-performance and innovative organization.
• Drive engagement/collaboration in a matrix organization, fostering strong partnerships – internal and external, and helping build diverse teams.
10 + years of experience in data management and data strategy in industry
Hands on experience with managing Data Ownership and Capability Building in many of biopharma data domains e.g. omics, compounds, diseases, patients, payers, CROs, sites, trials, KOLs, HCPs, EMR, EHR, claims, patient journeys, employees, contracts, vendors, products, etc
Demonstrated leadership of use case based agile implementation of Data Ownership and Capability Building in cross-organizational and cross-functional teams.
Proven track record in implementing a data strategy program from scratch in Pharma or o multi-business org.
The ideal candidate will have:
Understanding of data driven target identification and links between target and disease, differentiated efficacy, availability and predictive nature of biomarkers.
Understanding of data driven tissue identification towards adequate bioavailability and tissue exposure, definitions of PD biomarkers, preclinical and clinical PK/PD and target liability
Understanding of data driven Safety Analytics towards differentiated and clear safety margins, secondary pharmacology risk, reactive metabolites, genotoxicity, drug-drug interactions, and target liability
Understanding of protocol design and data driven Patients Analytics to identify most responsive population, risk-benefit for given population
Understanding of data driven commercial potential for differentiated value proposition verses future standard of care, focus on market access, etc.
Understand personalized healthcare strategy, digital medicines, including diagnostic and biomarkers.
Self Service Rapid Data Capture capability for data acquisition and transport pipelines between the Data Sources
Managed Multi-Tenant Data Lake capability to store raw and prepared data for analytics using different data stores optimized for various workloads in a legacy environment.
Understand need for immutability and movement Conformed Data Layer and need for lightweight modeling. Understand value of Graph stores and linked data across sources. Know when/why of Data Marts for purpose driven regulatory and non-regulatory use cases.
Data Curation capability with deep understanding of structural, syntactic and semantic heterogeneous in data sources
Data Distribution capability through APIs for Apps to Analysts to semantically search and find data assets.
Enable Extract, Transform, Load (ETL/ELT) capability to pull data out of one source, transform it
Workflow capability that operationalizes the data supply chain and data usage across the data ecosystem and the manner in which users/applications interact with system
Data Quality and profiling capability to help identify and govern condition of a set of values of qualitative or quantitative variables.
Understand data Ownership capability for applying quality control discipline to assessing, managing, using, improving, monitoring, maintaining, and protecting organizational information.
Backup and archiving capability so to restore the original after a data loss event
Security and Access capability to prevent unauthorized access, use, disclosure, disruption, modification, etc of information
DevOps & monitoring capability ensure that the platform is operating smoothly
Send an expression of interest and CV to the listed contact.