Associate Director, Oncology Clinical Intelligence & Applied Analytics

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

Job summary

AstraZeneca is seeking a senior quantitative scientist to turn complex real-world and clinical data into evidence guiding Phase 3 investments, trial design, and patient access. The role emphasizes causal inference, multimodal modeling, and regulatory-ready analyses in oncology, with emphasis on reproducibility and impactful decision support.

The position involves collaboration across Hematology teams, platform and tooling groups, and Translational Science to embed AI-powered insights into

Qualifications

• PhD or equivalent in a quantitative field (e.g., epidemiology, biostatistics, computational biology).
• 5+ years in evidence strategy, RWE or advanced analytics/Data science.
• Strong causal inference and observational design foundations.
• Hands-on ML and multimodal modeling with heterogeneous data.
• Experience building external control arms or trial simulators.
• Understanding regulatory and HTA evidence standards.
• Proficiency in Python, R, SQL; familiarity with cloud environments.

Responsibilities

• Prioritize evidence gaps with Hematology stakeholders and global teams across product lifecycle.
• Apply ML and causal inference to patient stratification and trial design questions.
• Develop multimodal patient models integrating clinical, genomic, imaging, and real-world data.
• Produce decision-ready insights on care pathways, benchmarking, and unmet needs.
• Collaborate with platform teams to deploy new analytics tools and AI approaches.
• Align outputs with regulatory, development, and access milestones; engage Translational Science and Clinical Development.

Skills

Python

R

SQL

Causal inference

ML/DL

Cloud platforms

External control arms

Education

PhD or equivalent in quantitative field

Tools

Python

R

SQL

Cloud tooling

Job description

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

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.

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.

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.

If you are ready to lead evidence into action and elevate Hematology decisions with rigorous, AI-powered analytics, step forward and make your impact today!

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

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.

Date Posted

11-Sep-2026

Closing Date

24-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.