Senior Research Associate, Genetic Pharmacology, Translational Research

Senior Research Associate, Genetic Pharmacology, Translational Research
The Opportunity:

We are seeking a Senior Research Associate specialized in data science and computational biology to join Genetic Pharmacology group. The Genetic Pharmacology group focuses on translational research developing platforms and technology that translate genetic insights into actionable strategies for targeted cancer therapy. The successful candidate will develop analytical tools and data management systems with interactive visualizations, assist with building statistical methods, and contribute to bioinformatics pipelines to support preclinical cancer research. Prior experience with LIMS (Benchling), app development with statistical modeling (RShiny), and pipelines for high-throughput genomic data is highly desirable.

Specifically, you will be responsible for:

Bioinformatic Analytics:

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  Conduct QC and analytics of NGS data, including across functional genomics screens, targeted amplicon sequencing, WES, WGS, RNA-seq, single-cell RNA-seq, and spatial transcriptomics projects.
  
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  Contribute to development and application of computational and statistical approaches to analyze preclinical studies focused on oncology biomarker discovery, patient stratification, and prediction of therapeutic responses.
  
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  Work closely with other cross-functional teams to curate, integrate, and visualize datasets.
  

Data Tool Development:

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  Build and maintain robust, automated, and scalable bioinformatics pipelines.
  
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  Define features and specs for data munging and visualization apps.
  
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  Develop and service user-friendly data analytics tools.
  

LIMS & Data Governance:

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  Build out and leverage Benchling LIMS for tracking of biological entities, experiments, data and metadata in collaboration with Information Sciences and Research teams.
  
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  Participate in digital transformation initiatives, ensuring adherence to FAIR data principles and addressing gaps in data harmonization and governance.
  

Project and Stakeholder Management:

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  Communicate with cross-functional stakeholders to identify data analysis gaps, needs, and opportunities for increased effectiveness and efficiency.
  
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  Establish and manage analytics projects.
  

Required Experience, Skills, and Education:

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  M.S. in a quantitative discipline (Bioinformatics, Data Science, Computational Biology, Bioengineering, Biomedical Engineering, Biostatistics, Statistical Genetics, Systems Biology, or a related field) with 1+ years of post-graduate experience focusing on oncology; Alternatively, Bachelor’s degree with 5+ years of relevant quantitative and oncology experience.
  
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  Proficiency in Python, R for data analysis and visualization.
  
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  Experience in webapp development for data visualization and statistical analytics (e.g. RShiny, Dash)
  
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  Expertise in biological software (e.g. Benchling, DNANexus, Rosalind).
  
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  Demonstrated ability to integrate large-scale multi-source biological datasets.
  
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  Familiarity with statistical methods for biological data applications.
  
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  Excellent communication skills to collaborate in a highly dynamic and cross-functional work environment.
  

Preferred Skills:

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  Experience with data modeling (e.g. entity-relationship diagraming) and development of Benchling LIMS registry (entity schemas, result tables, etc) is preferred.
  
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  Experience with prognostic and predictive biomarker discovery for oncology therapeutics development.
  
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  Expertise in functional genomics screens, targeted amplicon sequencing, WES, WGS, RNA-seq, single-cell RNA-seq, and spatial transcriptomics projects data sets and analysis methods focused on cancer biology (e.g. using data derived from cancer cell lines, organoids, or tumors).