Senior Scientist – Oncology Targeted Discovery (Drug Conjugates)

AstraZeneca•Waltham, MA

8d•Hybrid

About The Position

At AstraZeneca, we put patients first and strive to meet their unmet needs worldwide. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality. If you are swift to action, confident to lead, willing to collaborate, and curious about what science can do, then you’re our kind of person. Recognizing the importance of individualized flexibility, our ways of working allow employees to balance personal and work commitments while ensuring we continue to create a strong culture of collaboration and teamwork by engaging face-to-face in our offices 3 days a week. Our head office is purposely designed with collaboration in mind, providing space where teams can come together to strategize, brainstorm and connect on key projects. Our dedication to sustainability is also central to our culture and part of what makes AstraZeneca a great place to work. We know the health of people, the planet and our business are interconnected which is why we’re taking ambitious action to tackle some of the biggest challenges of our time, from climate change to access to healthcare and disease prevention. Introduction to role: Are you ready to transform multi-omic complexity and AI-driven insights into targeted cancer medicines that change patient outcomes? As a Senior Scientist focused on drug conjugates, you will bridge advanced computation and wet-lab validation to discover novel targets, decode mechanisms of action, and deliver predictive biomarkers that guide clinical decision-making. Your work will directly influence which programs advance and how we match therapies to the patients most likely to benefit. You will join a fast-moving oncology team with a bold pipeline spanning multiple indications. Partnering closely with experts across biology, chemistry, data science, and clinical development, you will convert complex datasets into testable hypotheses, design decisive experiments, and translate results into strategies for patient selection and combination therapy. Do you thrive at the interface of computation and experiment, turning insights into decisive actions?

Requirements

Candidate must hold a minimum of 1 year proven experience with a postdoctoral degree, or a minimum of 3 years industry experience with a master's degree.
Sophisticated bioinformatics analysis and data mining across public datasets (e.g., TCGA, CCLE), large consortium datasets, and proprietary clinical data to identify targets, biomarkers, combination partners, and dosing regimens.
Oversight of high-quality sample generation for multi-omics pipelines including RNASeq, scRNASeq, ATACSeq, proteomics, and phospho-proteomics.
Expertise in cancer cell signaling pathways to interpret proteomic and transcriptomic changes.
Proven ability to analyze and interpret high-dimensional single-cell RNASeq to uncover heterogeneity and produce actionable hypotheses.
Experience leading design and analysis of transcriptomic studies, including single-cell and spatial approaches, to map the tumor microenvironment.
Utilization of AI/ML tools to elucidate mechanism of action for therapeutics, specifically ADCs.
Identification of clinically relevant, tractable drug targets in hematologic and solid tumors and development of comprehensive intervention strategies.
Development of robust molecular signatures and biomarkers predictive of performance versus resistance to optimize patient stratification in heme and solid tumors.
Reverse translation of key clinical findings to inform predictive in vitro preclinical model development in heme and solid tumors.
Translation of bioinformatics findings into laboratory experiments to advance project goals, propose new targets, and inform go/no-go decisions.
Delivery of high-quality data packages that define mechanism of action and therapeutic efficacy in heme and solid tumors.
Advanced microscopy skills, including fluorescence-based live-cell imaging and immunofluorescence staining, to validate computational predictions.

Nice To Haves

Ph.D. in Bioinformatics, Computational Biology, Cancer Biology, or a related field.
Practical experience with ADC target selection criteria, linker/payload considerations, and resistance mechanisms.
Proficiency in R and/or Python, workflow management (e.g., Snakemake, Nextflow), version control, and scalable/cloud computing for multi-omic analytics.
Experience with spatial transcriptomics technologies and image analysis workflows that integrate with single-cell data.
Familiarity with building predictive biomarker models and patient stratification algorithms and validating them in preclinical systems.
Strong cross-functional communication, scientific storytelling, and the ability to influence decision-making with clear data narratives.
Prior leadership in cross-disciplinary studies that connect computation, assay development, and disease biology to deliver decision-grade data.

Responsibilities

Bioinformatics Discovery: Mine and integrate diverse datasets (public, consortium, proprietary clinical) to identify novel targets, predictive biomarkers, rational combinations, and optimized dosing strategies.
Multi-omics Sample Generation: Oversee high-quality sample generation for RNASeq, scRNASeq, ATACSeq, proteomics, and phospho-proteomics to ensure reliable downstream analysis.
Cancer Signaling Interpretation: Apply deep knowledge of cancer signaling pathways to interpret proteomic and trhifts that reveal mechanisms and vulnerabilities.
Single-Cell Analytics: Analyze and interpret high-dimensional scRNASeq data to uncover cellular heterogeneity and generate tractable biological hypotheses.
Spatial and Transcriptomic Study Leadership: Design and analyze single-cell and spatial transcriptomic studies to map the molecular landscape of the tumor microenvironment.
AI/ML for Mechanism of Action: Utilize AI/ML tools to elucidate mechanisms of action for therapeutics, with emphasis on antibody-drug conjugates.
Target Identification and Strategy: Identify clinically relevant, tractable targets across hematologic and solid tumors and propose comprehensive intervention strategies.
Biomarker Development: Develop robust molecular signatures and biomarkers predictive of response versus resistance to optimize patient stratification across tumor types.
Clinical Reverse Translation: Reverse translate key clinical findings to inform and refine predictive in vitro preclinical models in both heme and solid tumors.
Translational Integration: Translate bioinformatics-derived findings into laboratory experiments to progress projects, propose new targets, and support go/no-go decision-making.
Data Package Delivery: Deliver high-quality data packages that define mechanism of action and therapeutic efficacy and inform portfolio strategy.
Microscopy Validation: Use advanced microscopy, including fluorescence-based live-cell imaging and immunofluorescence staining, to validate computational predictions.