Job Description

Description :

The candidate will be responsible for leveraging computational methods to advance drug discovery from target identification through clinical strategy design. While the focus will be on utilizing predictive modelling to support neurology-related translational science, the candidate will also be providing general bioinformatics expertise to help directly guide therapeutic research across a variety of programs and platforms.

The candidate will join an existing (and evolving) global data science group at Company and will encounter a corporate culture that is very motivated to apply computational biology and other data-driven approaches to R&D at every level of the organization. Given the expanding scope of the group’s operations, the successful candidate will have the opportunity to help shape the direction and focus of our bioinformatics team as we position to best support our neurology business group.

Essential Functions

Design, implement, and apply statistical, network, and machine learning techniques to analyze and integrate large, complex datasets (e.g., RNA-seq, scRNAseq, proteomics)

Maintain a keen awareness of recent developments in data science and machine learning applied to drug discovery and influence the introduction of novel computational techniques

Understand the usage of different statistical modelling approaches, their biases, how and when to apply them, decide which method/s to use for a particular analysis and data set

Apply predictive modeling (including AI/ML) to leverage single cell approaches

Translate in vitro/vivo findings to bolster our understanding of human disease pathology, patient quality of life, and potential disease subtypes leading to patient stratification

Clearly communicate key findings through interactive data visualizations and presentations

Work cross-functionally among R&D teams, workstreams, and programs to provide statistical support and bioinformatics expertise across the neurology portfolio

Work independently and as part of a team, with a willingness to learn new or unfamiliar types of analysis approaches as needed

Mentor/coach team members and peers in technical and scientific aspects

Requirements

PhD with 5 – 7 years of industry or relevant experience, or MS with 8 – 10 years of relevant experience with strong expertise in data science and machine learning required

Knowledge of programming languages, i.e. Python and R, and Unix / high-performance computing environment, including software development practices such as version control

Experience with distributed computing, database (e.g., SQL) or relational databases (e.g., PostgreSQL), and Amazon Web Services is preferred

Experience analyzing publicly available as well as internally generated transcriptomic (bulk RNA-seq, scRNA-seq) and proteomic data for drug discovery and development processes

Statistical knowledge of probability distributions, sampling techniques, causal inference and other statistical concepts

Proven experience with Deep Learning, Neural Networks and their application. Familiarity with some deep learning libraries such as Keras, TensorFlow, and PyTorch is a plus

Experience with drug discovery in neurology and/or immunology is preferred

Strong communications skills, including data visualization experience (RShiny, Spotfire, Tableau)

Eager to work collaboratively in a team and to both give and receive feedback to support professional development for self and others

The ability to thrive in a flexible, goal-oriented environment is required

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