Senior Scientist – Oncology Targeted Discovery; Drug Conjugates

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?

• 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.

• 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.

• Ph.D. in Bioinformatics, Computational Biology, Cancer Biology, or a related field.
• Practical…