Associate Principal Scientist, Bioinformatics, Immuno-Oncology Analytics

MSD Malaysia; Boston (MA); Hybrid; USD 80,000 - 130,000; Full time

Job summary

An established industry player seeks an Associate Principal Scientist to join its innovative data science team. This role focuses on analyzing multi-modal genome-scale molecular data to drive decisions in oncology. The ideal candidate will leverage their expertise in statistical learning and data mining to inform biomarker discovery and patient stratification. With a commitment to diversity and equal opportunity, this company fosters an inclusive environment where collaboration thrives. If you're passionate about transforming cancer research through data-driven insights, this opportunity is perfect for you.

Qualifications

• 3+ years experience in computational methods in cancer biology.
• Expertise in analyzing high-dimensional multimodal molecular data.

Responsibilities

• Analyze multi-modal genome-scale molecular data for oncology.
• Visualize clinico-genomic datasets for biomarker discovery.

Skills

Statistical learning

Data mining

R/Python coding

Communication skills

Education

PhD in Engineering

PhD in Physics

PhD in Mathematics

PhD in Bioinformatics

PhD in Computational Biology

Tools

R

Python

Matlab

Job description

Associate Principal Scientist, Bioinformatics, Immuno-Oncology Analytics

Apply remote type Hybrid locations USA - Massachusetts - Boston (MA Parcel B Laboratory) time type Full time posted on Posted 3 Days Ago time left to apply End Date: May 9, 2025 (7 days left to apply) job requisition id R346296

Job Description

The Translational Genome Analytics group within the Data, AI & Genome Sciences Department is recruiting an Associate Principal Scientist to join our data science team. We seek an experienced computational scientist to analyze multi-modal genome-scale molecular data and inform decisions across our oncology pipeline. The successful candidate will:

• Enable reverse translation from clinical datasets to inform biomarker discovery, combination strategies, and novel target identification in molecularly defined patient populations with high unmet medical need.
• Analyze and visualize large clinico-genomic datasets, including bulk RNAseq, WES/WGS, epigenetics, single-cell RNAseq, and proteomics from clinical trials and real-world data.
• Use analytical methods and predictive modeling to define molecular subtypes of patients with distinct tumor microenvironments, molecular drivers, and outcomes.
• Present data analyses to stakeholders across various internal teams, including discovery, translational, and clinical development.

Minimum Education Requirement:

• PhD in Engineering, Physics, Mathematics, Bioinformatics, Computational Biology, or related field with a strong computational and statistical focus.

Required Experience and Skills:

• Minimum 3 years post-PhD experience applying computational methods in cancer biology within pharma, biotech, or academia.
• Expertise in statistical learning and data mining for high-dimensional multimodal molecular data in oncology/immuno-oncology.
• Hands-on experience with algorithms for large genetic, genomic, immunogenomic, and clinical datasets (e.g., TCGA, GTEx, DepMap).
• Proficiency in coding in R/Python or Matlab, adhering to reproducible analysis practices.
• Strong communication skills; ability to influence decisions with data-driven insights; attention to detail.
• Independent, collaborative, and flexible mindset.

Preferred Skills:

• Experience analyzing genome-scale data from clinical trials.
• Deep understanding of cancer biology concepts as reflected in molecular data.
• Experience in industry and cross-disciplinary collaboration.
• Publication record in high-impact scientific journals.

Our company is committed to diversity and equal opportunity employment, adhering to all applicable laws and regulations. We provide accommodations for applicants with disabilities and are dedicated to inclusive hiring practices.