Associate Director, Hematology Clinical Intelligence & Applied Analytics

Biopharma Careers; Waltham (MA); On-site; USD 145,000 - 217,000; Full time

Job summary

AstraZeneca seeks a senior quantitative scientist to turn complex clinical and real-world data into decision-ready evidence for Hematology, influencing Phase 3 investments, trial design and patient access.

You will build multimodal models, apply causal inference, and collaborate with Global Product Teams to deliver evidence across development and payer strategies with a focus on reproducibility and regulatory standards.

Qualifications

• PhD or equivalent in epidemiology, biostatistics, computational biology, machine learning, health data science, or related field.
• 5+ years of experience spanning evidence strategy and real-world data/analytics.
• Strong foundation in causal inference and observational study design (propensity scores, IVs, target trial emulation).

Responsibilities

• Evidence Gap Prioritization with stakeholders and Global Product Teams for Phase 3 decisions and trial design.
• Apply machine learning and causal inference to patient stratification and external control arms.
• Develop multimodal patient models integrating clinical, genomic, imaging and real-world data.
• Produce decision-ready insights on standard of care, patient pathways, and benchmarking.
• Collaborate with platform teams to bring new tools into evidence generation.
• Connect strategic evidence needs with technical delivery across development, regulatory, and access milestones.

Skills

PhD in quantitative field

Experience 5+ years

Causal inference

Python, R, SQL

External control arms

Multimodal modeling

Education

PhD or equivalent

Job description

Are you ready to turn complex clinical and real-world data into evidence that shapes pivotal decisions in Hematology? Do you want your analytics to directly influence Phase 3 investments, trial design, and patient access?

In this role, you will operate where evidence generation meets hands‑on analytics, partnering with stakeholders to pinpoint the highest‑value evidence gaps and leading the delivery that closes them. You will move seamlessly from portfolio‑level prioritization with Global Product Teams to building rigorous multimodal patient models or external control arm analyses that inform development and access.

Hematology is growing rapidly. As the technical specialist for clinical intelligence and real‑world evidence, you will translate complex data into decision‑ready insights, build persistent intelligence that compounds across use cases, and help steer how the portfolio advances for patients.

Accountabilities

• Evidence Gap Prioritization: Partner with Hematology stakeholders and Global Product Teams to identify and prioritize evidence needs across the product lifecycle, including Phase 3 investment decisions, subpopulation discovery, and trial design.
• AI and Causal Inference Analytics: Apply machine learning and causal inference to deliver robust answers on patient stratification, external control arm construction, prognostic risk adjustment, and treatment effect heterogeneity, ensuring analyses meet regulatory and HTA expectations.
• Multimodal Patient Models: Deliver and validate patient‑level models that integrate clinical, genomic, imaging, and real‑world data for deployment in clinical trials or routine care, with emphasis on reproducibility and rigorous validation.
• Decision‑Ready Insights: Turn internal and competitor trial data, alongside real‑world data, into clear insight on standard of care, patient pathways, benchmarking, and unmet need to sharpen development and access strategies.
• Platform Enablement: Work with platform and tooling teams to bring new tools, agents, and experimental approaches into evidence generation, and build Hematology‑specific intelligence that persists within the Phase 3 Investment Decision Intelligence Foundation.
• Cross‑Functional Collaboration: Connect strategic evidence needs with technical delivery, aligning outputs to development, regulatory, and access milestones; collaborate with Translational Science and Clinical Development to incorporate novel signals such as digital endpoints, pathology AI, and biomarker panels.

Essential Skills/Experience

• Advanced degree (PhD or equivalent) in a quantitative discipline - epidemiology, biostatistics, computational biology, machine learning, health data science, or a related field.
• 5+ years of experience spanning both evidence strategy and real‑world evidence and/or advanced analytics and data science.
• Strong methodological foundation in causal inference and observational study design, including propensity score methods, instrumental variables, target trial emulation, and comparative effectiveness research.
• Hands‑on experience with machine learning and multimodal modeling, including supervised and unsupervised methods, deep learning for imaging or molecular data, and integration of heterogeneous data types into patient‑level models.
• Experience building or contributing to external control arms, trial simulators, or prognostic models using real‑world and/or clinical trial data.
• Understanding of regulatory and health technology assessment (HTA) evidence standards, with the ability to design analyses that meet the evidentiary bar for submissions and payer engagement.
• Proficiency in Python, R, and SQL, and familiarity with cloud‑based analytics environments.

Desirable Skills/Experience

• Domain expertise in hematology or oncology and familiarity with disease‑specific endpoints, pathways, and standards of care.
• Track record of influencing cross‑functional strategy with evidence, engaging product and clinical leaders to drive decisions.
• Experience deploying analytics into clinical trial operations, submissions, or payer engagements.
• Publications, conference presentations, or open‑source contributions in causal inference, multimodal modeling, or real‑world evidence.
• Hands‑on experience with cloud platforms and MLOps practices to scale models and pipelines.
• Experience integrating digital endpoints, pathology AI, or biomarker panels into evidence strategies.
• Ability to design reusable data and model assets that generalize across indications and studies.

Why AstraZeneca

Here, specialists sit close to decision‑making and see their work move quickly from idea to impact. You will collaborate with diverse, down‑to‑earth experts who bring different perspectives to the same table, using cutting‑edge analytics to shape how medicines reach patients. We blend rigor with creativity, pairing ambitious goals with the support and kindness needed to achieve them. You will grow in an environment that invests in your development, values curiosity, and gives you the autonomy to build solutions that scale across the enterprise while staying focused on what matters most, better outcomes for patients.

Compensation and Benefits

• The annual base pay (or hourly rate of compensation) for this position ranges from $144,648 to $216,973. Our positions offer eligibility for various incentives, an opportunity to receive short‑term incentive bonuses, equity‑based awards for salaried roles and commissions for sales roles.
• Benefits offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans.

As AstraZeneca continues to put patients at the forefront of our mission, we are excited for our move to Kendall Square/Cambridge in 2026. Find out more information here: Kendall Square Press Release

Date Posted: 10‑Sep‑2026

Closing Date: 22‑Sep‑2026

Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form.