Role Purpose 

The Manager / Associate Manager, Patient Business Model & Insights is responsible for designing a patient-level commercial model for rare diseases and for generating patient insights from real-world data (RWD) and other data sources. As the core role of Patient Ladder Harmonization, this position aligns the Patient Ladders, KPIs, and patient-data practices that currently differ by disease, brand, and business unit into a common framework, so that the patient journey and business progress can be described with the same definitions and the same measures across all diseases. 

The role holds design and insight as one accountability. It designs patient-journey stages, KPIs, data models, and disease-archetype strategies in a form that can be measured with data. At the same time, it designs analyses of RWD, internal data, and public information according to those definitions, and validates and interprets the results executed by data scientists and external analytics partners to generate insights — such as the number of patients at each stage, stage-to-stage conversion rates, lead time to diagnosis and treatment initiation, and persistence — and connects them to decisions on brand planning, CRM strategy, marketing initiatives, and resource allocation. 

The role operates on the premise of a highly regulated environment and of diseases with extremely small patient populations. It builds into the design, as preconditions, the permitted scope of use according to how patient data was obtained and whether consent exists, the risk of re-identifying individuals, data licensing and permitted-use terms, and the regulations governing direct engagement with patients. Working with legal, compliance, medical, and privacy functions, it operates the model and its insights within appropriate governance and human decision-making frameworks. 

Key Responsibilities 

Business Model Design 

The role defines common patient-journey stages (such as suspicion, diagnosis, treatment selection, treatment initiation, persistence, and switching), maps them against the existing Patient Ladder of each disease and business unit, and builds a stage framework that can be used in common across all diseases. 

The role designs common definitions and calculation methods for KPIs such as "new patient," "treatment initiation," "persistence," "discontinuation," and "switch," so that stage-to-stage conversion rates, lead times, and persistence can be calculated in the same way. Where definitions differ between business units, the role makes the differences visible and leads alignment. 

The role contributes to the design of a data model that classifies patient data by how it was obtained and whether consent exists, and that specifies, for each class, the permitted purposes, level of granularity, storage method, authorized viewers, and representation in CRM. It builds the elements required for governance — consent metadata, audit logs, and upper limits on the granularity of de-identified data — into the design from the outset. For identifiable data obtained with patients' own consent, the role is responsible only for designing how the data is held, its consent metadata, and access rights; the role itself does not view individual patient records. 

The role defines disease archetypes according to disease characteristics (for example, diseases with many competitors where persistence and switching matter most, ultra-rare diseases with a single therapy where finding each patient matters most, and diseases with a large undiagnosed population where the path from diagnosis to treatment selection matters most), and designs CRM strategies and marketing models that, on top of the common framework, vary the priority stages, tracked indicators, and initiatives by archetype. 

The role designs the mechanisms that turn CRM signals into initiatives for healthcare professionals, institutions, and patients (limited to what regulations permit) — including next-best-action design, omnichannel orchestration, and modular messaging — together with the methods for measuring their effect, including how control groups are set. 

The role runs pilots of the designed model in selected diseases, measures and evaluates the results, and feeds them into roll-out to other diseases and business units and into revisions of the standard. 

Patient Insight Generation from RWD and Other Data 

The role starts from brand and business questions — not from available data or tools — and designs analytical themes after determining which questions each data source can and cannot answer. 

The role designs analyses that combine commercial claims databases, public statistics and open data, internal data (such as CRM, patient profiles, and patient support programs), and published literature and guidelines to generate patient insights such as the number of patients at each stage, stage-to-stage conversion rates, lead time to diagnosis and treatment initiation, persistence and discontinuation, referral pathways, and healthcare delivery structures. Data extraction and aggregation are performed by data scientists and external analytics partners; the role itself does not hold direct access rights to internal or purchased data. 

The role assesses the limitations of each data source — including coverage, representativeness, accuracy of diagnosis and prescription records, follow-up period, and biases arising from how persistence is measured — and triangulates results across multiple data sources to show the range and confidence of estimates. For diseases with extremely small patient populations, the role determines the granularity and presentation of results in light of estimation uncertainty and re-identification risk. 

The role works with data scientists and external analytics partners to specify analyses (definitions of target populations, metric definitions, analytical logic, assumptions, and limitations), reviews deliverables, and validates the soundness of the logic. 

The role translates findings and their limitations in a way that non-specialist stakeholders understand without misinterpretation, makes data provenance, coverage, and confidence explicit, and supports decision-making while preventing over-interpretation. 

The role defines how KPIs are measured during pilots and after roll-out, and, based on the measured results, analyzes gaps between model assumptions and actual results, and reflects them in revisions of the business model. 

Driving Harmonization and Governance 

The role works with stakeholders across marketing, sales, medical, patient support, omnichannel, data platforms, IT, legal, compliance, privacy, and procurement to agree common definitions, data models, and operating rules, and to embed them in each business unit. 

The role organizes regulatory and internal-policy issues — concerning direct engagement with patients, disease awareness, patient support programs, patient information obtained from healthcare professionals, use of secondary data, and use of post-marketing surveillance and registry data — and prepares material in a form that the relevant functions can decide on. Legal determinations are made by those functions; the role reflects their conclusions in the design. 

The role maintains common definitions, data dictionaries, analysis specifications, and records of decisions as version-controlled documents, ensuring traceability that withstands audit. 

The role assesses the value and utilization of data, and supports decisions on purchasing, renewing, and consolidating data based on actual business use. 

The role clarifies requirements with external data vendors, analytics partners, and consulting firms, assesses data fit and quality, and reviews deliverables. 

The role makes practical use of AI and digital tools to accelerate information gathering, organization of literature and data, documentation, and analysis. It does so within appropriate governance, compliance, privacy, security, and human decision-making frameworks, and validates outputs before business use. 

The role connects with APJ and global patient data model and CRM initiatives, clarifies differences from Japan's regulatory and data environment, and separates what can be standardized in common from what must be designed specifically for Japan. 

Manager-level Expectations 

In addition to the responsibilities above, the Manager is expected to independently lead the business model design and patient insight generation of Patient Ladder Harmonization from problem definition through design, analysis, alignment, pilot, and roll-out. Where definitions or priorities conflict between business units, the Manager is expected to steer the discussion constructively with data and business rationale, and to present senior management with the options and issues needed for decision-making. 

The Manager is also expected to judge whether a request warrants new analysis or should reuse existing definitions and deliverables, and to influence decisions on data investment. The Manager oversees the deliverables of external vendors and partners, supports team prioritization, and coaches Associate Managers and stakeholders in the correct interpretation and application of patient data and RWD. 

Associate Manager-level Expectations 

In addition to the responsibilities above, the Associate Manager is expected to carry out design and analysis work within a defined scope under the guidance of more senior team members. This includes creating and maintaining stage-to-KPI mapping tables and data dictionaries, drafting analysis specifications, cross-checking analytical results and confirming their validity, turning analytical results into presentation materials, coordinating with stakeholders, documenting issues and decisions, and operating pilots and tracking their metrics. 

The Associate Manager is expected to build solid knowledge of patient journeys and disease characteristics, the structure and limitations of RWD, KPI and analytical-logic definitions, regulations and governance concerning patient data, and commercial decision contexts, while progressively increasing independence in stakeholder support, analytical ownership, and problem solving. 

Experience — Mandatory 

Hands-on experience in pharmaceutical, healthcare, life sciences, healthcare data, or related commercial, analytical, or consulting fields, including practical experience analyzing healthcare data such as medical or pharmacy claims, electronic medical records, or registries, or designing, commissioning, and reviewing such analyses. 

Experience designing commercial models, patient journeys, brand plans, CRM, or KPI frameworks, and aligning cross-functional stakeholders to put them into practice. 

Experience — Preferred 

Experience in rare disease or specialty therapy areas; experience with patient support programs, patient-level CRM, omnichannel, or epidemiology and outcomes research; practical experience with privacy and regulatory compliance concerning patient data; and experience in projects coordinated with global or regional teams. 

Qualifications 

A bachelor's degree or higher in Marketing, Business, Life Sciences, Pharmacy, Public Health, Epidemiology, Data Analytics, Health Informatics, or a related field is required. A master's degree (for example, in Public Health, Epidemiology, Business, or Data Science) is preferred. 

Skills — Skill-set Mandatory 

• Ability to map the patient journey to business stages and translate them into measurable KPIs with clear definitions and calculation methods. 

• Understanding of the structure, coverage, representativeness, and biases of RWD, and the ability to judge which questions a given data source can and cannot answer. 

• Ability to define target populations, metrics, and analytical logic in a documented and reproducible way, and to review and validate analytical outputs. 

• Ability to triangulate multiple data sources and show the range and confidence of estimates, with an understanding of estimation uncertainty and re-identification risk in small patient populations. 

• Understanding of the commercial contexts of pharmaceutical marketing, including brand planning, targeting, customer engagement, CRM, and performance measurement. 

• Understanding of privacy, data licensing and permitted use, regulations on pharmaceutical information provision and advertising, and compliance requirements concerning patient data. 

• Ability to communicate conclusions, assumptions, and limitations clearly to non-specialist stakeholders and to build cross-functional alignment. 

• Working familiarity with analytical and visualization tools sufficient to define requirements, review deliverables, and validate logic. Advanced statistical modeling is expected of data science roles and is not required for this position. 

• Strong project management skills and attention to detail, with the ability to manage multiple priorities and deliver on time. 

• Practical experience applying AI to information gathering, documentation, and analytical efficiency is preferred. 

Manager-level expectations: Typically requires broader experience in patient-level commercial models, RWD analytics, healthcare data, or consulting, with a demonstrated track record of leading initiatives, influencing senior stakeholders, and delivering results. 

Associate Manager-level expectations: Typically requires foundational experience in analytics, healthcare data, commercial planning, or related support roles, with a demonstrated track record of contributing to project delivery, documentation quality, and stakeholder support. 

Core Competencies 

• Patient centricity 

• Business model design 

• Real-world data fluency 

• Insight translation 

• KPI and definition design 

• Data-driven decision-making 

• Commercial acumen 

• Data governance and compliance 

• Cross-functional collaboration 

• Communication and stakeholder engagement 

• Alignment and standardization 

• Execution discipline 

• Continuous improvement 

• AI and digital enablement literacy 

Languages — Mandatory 

Japanese: Native 

English: Business level (discussions with global and APJ teams; reading and writing English materials) 

Date Posted

Closing Date

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