Insights analyst

AstraZeneca is a global, science-led, patient-focused biopharmaceutical company that focuses on the discovery, development, and commercialisation of prescription medicines in Oncology, Rare Diseases, and BioPharmaceuticals, including Cardiovascular, Renal & Metabolism, and Respiratory & Immunology. We are committed to pushing the boundaries of science to deliver life-changing medicines, and we believe that

data science and artificial intelligence

are central to how we will redefine drug discovery and patient care over the next decade. About the Role We are looking for an exceptional

Scientist or Senior Scientist

to join our growing

AI/ML for Translational and Clinical Sciences

team, working at the intersection of

digital twins, foundation models, and multimodal clinical data

. In this role, you will design and build next-generation machine learning systems that predict clinical outcomes, discover novel biomarkers, and enable precision patient stratification across AstraZeneca's therapeutic areas. You will help build

patient-level digital twins

that integrate longitudinal clinical, imaging, genomic, proteomic, and real-world data—leveraging

foundation models

to reason across modalities and time. Your work will directly inform trial design, endpoint selection, and translational decision-making, ultimately accelerating the delivery of transformative therapies to patients. This is a highly collaborative role sitting at the interface of

Data Science, Clinical Development, Translational Medicine, and Biometrics

, with strong exposure to therapeutic area leadership. What You'll Do Digital Twin Development

: Design, train, and validate patient-level digital twin models that simulate disease trajectories and treatment response using longitudinal multimodal clinical data.

Foundation Model Research

: Contribute to the development, fine-tuning, and evaluation of foundation models (transformer-based, generative, and multimodal) tailored to clinical and biomedical data, including EHR, medical imaging, omics, and free-text clinical notes.

Clinical Outcome Prediction

: Build predictive and causal ML models for clinical endpoints, adverse events, disease progression, and treatment response, ensuring rigorous validation against prospective and external datasets.

Multimodal Data Integration

: Develop scalable pipelines and representation-learning approaches that unify structured clinical, genomic, transcriptomic, proteomic, imaging, and real-world evidence data.

Biomarker Discovery

: Apply interpretable ML and causal inference methods to identify and validate novel prognostic and predictive biomarkers from clinical trial and real-world datasets.

Patient Stratification

: Design ML-driven stratification strategies to support precision medicine hypotheses, enrichment trial designs, and companion diagnostic development.

Cross-Functional Collaboration

: Partner closely with clinicians, statisticians, translational scientists, bioinformaticians, and MLOps engineers to translate models into decision-grade tools embedded in R&D workflows.

Scientific Leadership

: Publish in top-tier venues ( Nature Medicine

,

NeurIPS

,

ICML

,

Cell Patterns

,

Lancet Digital Health ), represent AstraZeneca at external conferences, and contribute to strategic partnerships with academic and technology collaborators.

Regulatory & Ethical Rigour

: Ensure that models are developed in line with

GxP

,

model risk management

, fairness, privacy, and emerging regulatory guidance (FDA, EMA, MHRA) for AI/ML in drug development.

At the

Senior Scientist

level, you will additionally be expected to shape scientific strategy, mentor junior scientists, lead cross-functional workstreams, and act as a technical authority in digital twin and foundation model methodology across the portfolio. Essential Requirements PhD

in Computer Science, Machine Learning, Computational Biology, Biomedical Engineering, Biostatistics, Physics, or a closely related quantitative discipline

OR an MS in a comparable discipline with equivalent applied research experience

in AI/ML for healthcare or life sciences.

Demonstrable experience developing

machine learning or deep learning models applied to clinical, biomedical, or omics data

.

Strong proficiency in

Python

and modern ML frameworks (

PyTorch

,

JAX

, or

TensorFlow

), including experience with distributed training on GPU/TPU infrastructure.

Solid understanding of

transformer architectures

,

self-supervised learning

, and

foundation model

training or fine-tuning paradigms.

Experience working with

longitudinal clinical data

(EHR, clinical trials, registries) and familiarity with data standards such as

OMOP

,

CDISC (SDTM/ADaM)

,

FHIR

, or

DICOM

.

Strong grounding in

statistical inference

,

causal modelling

, or

survival analysis

, and a rigorous approach to validation and generalisation.

Track record of scientific output through

peer-reviewed publications, preprints, or open-source contributions

.

Excellent written and verbal communication skills, with the ability to explain complex methods to non-technical stakeholders.

Experience expectations by level: Scientist

: PhD with 0–3 years of relevant post-PhD experience,

or

MS with

4+ years

of relevant industry/research experience in applied ML for biomedical data.

Senior Scientist

: PhD with

5+ years

of relevant post-PhD experience,

or

MS with

8+ years

of relevant experience, and a demonstrated record of leading end-to-end ML projects and influencing scientific or product strategy.

Desirable Requirements Experience building or contributing to

digital twin

,

synthetic control

, or

mechanistic-ML hybrid

models in a healthcare or life-sciences setting.

Familiarity with

multimodal representation learning

, including vision-language models, graph neural networks, or time-series transformers.

Prior work with

multi-omics integration

(genomics, transcriptomics, proteomics, single-cell) and pathway-informed modelling.

Experience deploying models in

regulated environments

(GxP, SaMD) and familiarity with

model interpretability

,

uncertainty quantification

, and

fairness

frameworks.

Exposure to

cloud platforms

(AWS, Azure, GCP),

MLOps

tooling, and reproducible research practices (containers, workflow managers, experiment tracking).

Experience collaborating within

pharma R&D

,

clinical development

, or

academic medical centres

.

Why AstraZeneca At AstraZeneca, we are pioneering a new era of scientific discovery where

data, AI, and human ingenuity converge

to redefine what medicine can do. Our AI/ML community spans thousands of scientists and engineers working across the entire value chain—from target discovery to real-world evidence—supported by world-class compute, curated clinical datasets, and deep therapeutic expertise. You will have the rare opportunity to see your models move from prototype to production, influencing decisions that shape clinical trials and reach patients globally. We offer a

competitive salary

, performance bonus, share programmes, comprehensive health benefits, generous parental leave, learning and development budgets, and a strongly hybrid, inclusive working culture. Diversity & Inclusion AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status. How to Apply Please submit your CV together with a short cover letter outlining your relevant experience and motivation for the role. Applications will be reviewed on a rolling basis. The annual base pay for this position ranges from $72,120.00 - $108,181.20. 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 21-Sep-2026

Closing Date 27-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.