Job Description

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Help refine, develop, and build the
data analytics and computational strategy for ThinkBio.Ai®

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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.

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Analyze complex multi-omics data sets
across various parameters and connect insights and observations to actionable
hypotheses.

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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.

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Develop and apply tailored data
analytical and computational methods/techniques to advance both our clinical
and research programs.

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Develop novel computational platforms
and pipelines to identify novel therapeutic targets, and to discover biomarkers
for drug response, patient stratification

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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.

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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

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Ph.D. or Masters in Computer science,
Data science, Statistics, Bioinformatics or related fields.

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10+ years’ experience and technical
expertise in applied bioinformatics, computational biology, data science or
biostatistics.

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Robust working knowledge and
application of data analysis and modeling, data wrangling and data
visualization.

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Firm grasp of modern statistical
methods and machine learning techniques, and their applications to large-scale,
high throughput dataset analysis.

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Proficiency with R/ Bioconductor,
Python or equivalents, and relational databases (SQL, NoSQL).

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First-hand experience in
multi-parametric data mining, analysis and visualization in any biomedical
application.

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Exposure to multi-parametric data
mining experience for disease stratification/endotyping, target identification
and biomarker analysis.

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Experience and understanding of how
bioinformatics and data science can best be applied to speed up drug discovery.

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Basic understanding of biological
concepts and a familiarity with drug development process

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Knowledge of bioinformatic tools and
databases to analyze genomics and proteomics data

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Ability to manage projects with minimal
supervision, using creative and analytical thinking.

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Ability to drive highly collaborative
work across the organization and outside the company

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Excellent oral and written
communication.

Desirable Additional Experience

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

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Deeper knowledge/training/experience in
biomedical field.

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A minimum of 1-year research (academia
or industry) experience.

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Demonstrated experience in deep
learning and generative AI model based approaches such as bioinformatics
foundation models (BFMs).

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Experience in genomics,
transcriptomics, Next Generation Sequencing (NGS) analysis, single cell RNAseq,
flow cytometry or IHC based data processing.

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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.

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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).

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Managing a data analytics and
computational operating model that encompasses processes and technologies for
executing scalable data management solutions for various data types.