Data and Computational Biology Scientist

Key Responsibilities

• Help refine, develop, and build the data analytics and computational strategy for ThinkBio.Ai
  
• Develop and apply computational/statistical methods to analyze various inhouse and externally sourced data including those related to biological experiments or human population parameters such as disease burden, genetic variation, cause-effect association etc.
  
• Analyze complex multi-omics data sets across various parameters and connect insights and observations to actionable hypotheses.
  
• Synthesize provided data into actionable insights by supporting any needed computational, statistical, machine learning or modeling capabilities needed to enable strategic decision making and advance our clinical and research programs.
  
• Develop and apply tailored data analytical and computational methods/techniques to advance both our clinical and research programs.
  
• Develop novel computational platforms and pipelines to identify novel therapeutic targets, and to discover biomarkers for drug response, patient stratification
  
• Ability to integrate validated data analysis tools and pipelines from public resources to create robust data analysis and interpretation pipelines for visualization and integrative analysis, and interactive dashboards to create insightful visualization and interpretation of data.
  
• Work closely with other team members and partners to identify most critical data centered challenges and address them using cutting-edge computational, statistical and machine learning applications.
  

Required Qualifications and Skills

• Ph.D. or Masters in Computer science, Data science, Statistics, Bioinformatics or related fields.
  
• 10+ years’ experience and 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.

• Deeper knowledge/training/experience in biomedical field.
  
• A minimum of 1-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.