Senior Computational Biologist

At ThinkBio.AI we are adding Intelligence to Digital Biology. We leverage our deep expertise in Biotech, Pharma, Health Sciences, and AI, along with our experience in software development to provide platforms, toolkits, and solutions. Excellent opportunities are available to engage in cutting edge research using engineered biology-based approaches and frontier emerging areas of synthetic biology, to create transformative contributions in human drug discovery and development. We are looking for an enthusiastic and energetic Data and Computational scientist with a passion for learning and impacting human lives through scientific discoveries.

Job Description

• Lead, mentor, and manage a world-class team focused on biotech and pharma areas aligned with ThinkBio goals.
  
• Manage project delivery, working with customers and technology partners to ensure successful outcomes.
  
• Develop and implement data analytics and computational strategies for ThinkBio, advancing biotech and pharma initiatives.
  
• Analyze multi-omics and other complex data sets, providing actionable insights through advanced computational, statistical, and machine learning methods.
  
• Build computational platforms to discover novel therapeutic targets, identify biomarkers, and enhance drug discovery and development processes
  
• Integrate public data analysis tools into robust, scalable pipelines for visualization, interpretation, and interactive dashboards.
  
• Collaborate with cross-functional teams and stakeholders to solve critical data challenges using advanced analytics and machine learning.
  
• Contribute to the ThinkBio’s research and development through publications in relevant scientific journals.
  

Skills Required:

• Technical expertise in applied bioinformatics, computational biology, data science or biostatistics.
  
• Robust working knowledge and application of data analysis and modeling, data wrangling and data visualization.
  
• Firm grasp of modern statistical methods and machine learning techniques, and their applications to large-scale, high throughput dataset analysis.
  
• Proficiency with R/ Bioconductor, Python or equivalents, and relational databases (SQL, NoSQL).
  
• First-hand experience in multi-parametric data mining, analysis and visualization in any biomedical application.
  
• Exposure to multi-parametric data mining experience for disease stratification/endotyping, target identification and biomarker analysis.
  
• Experience and understanding of how bioinformatics and data science can best be applied to speed up drug discovery.
  
• Basic understanding of biological concepts and a familiarity with drug development process
  
• Knowledge of bioinformatic tools and databases to analyze genomics and proteomics data
  
• Ability to manage projects with minimal supervision, using creative and analytical thinking.
  
• Ability to drive highly collaborative work across the organization and outside the company
  
• Excellent oral and written communication.
  

Desirable Additional Experience

Experience in one or more of the following areas is highly desirable, but not essential.

• PhD in Computational Biology, Bioinformatics, data science related field.
  
• A minimum of 5+-year research (academia or industry) experience.
  
• Demonstrated experience in deep learning and generative AI model-based approaches such as bioinformatics foundation models (BFMs).
  
• Experience in genomics, transcriptomics, Next Generation Sequencing (NGS) analysis, single cell RNAseq, flow cytometry or IHC based data processing.
  
• Experience working with one or more of the following disciplines: synthetic biology, comparative genomics, population genetics, probabilistic modeling, population genetics, and quantitative modeling of biological systems.
  
• Experience with one or more of the following: P Snakemake, Nextflow, airflow, CWL, relational databases (SQL), GraphQL, distributed computing (AWS/Google Cloud), Docker, software version control (git).
  
• Managing a data analytics and computational operating model that encompasses processes and technologies for executing scalable data management solutions for various data types.