Computational Scientist

Position Title: Computational Scientist
Location: North Chicago
Contract: 12+ months with possible extension

Seeking a highly motivated and driven

data scientist to join our Quantitative, Translational & ADME Sciences (QTAS) team in North Chicago, IL . The QTAS organization

supports the discovery and early clinical pipeline through mechanistically investigating how drug molecules are absorbed, distributed, excreted, metabolized, and transported across the body to predict duration and intensity of exposure and pharmacological action of drug candidates in humans . Digital workflows, systems, IT infrastructure, and computational sciences are critical and growing components within the organization to help deliver vital results in the early pipeline.

This specific job role is designed to act as an SME (subject matter expert) for data science within the technical organization of QTAS.
For this role, the

successful candidate will have a substantial background in data and computer science with an emphasis on supporting, developing and implementing IT solutions for lab-based systems as well as utilizing computational methods . The

candidate should possess a deep knowledge in AI/Client, with a focus on both supervised (like neural networks, decision trees) and unsupervised learning techniques (such as clustering, PCA).

They must be adept at applying these

methods to large datasets for predictive modeling ; in this

context- drug properties and discovery patterns in ADME datasets . Proficiency in

model validation, optimization, and feature engineering

is essential to ensure accuracy and robustness in predictions. The role requires effective collaboration with interdisciplinary teams to

integrate AI insights into drug development processes.

Strong communication skills are necessary to convey complex AI/Client concepts to a diverse audience.

Key Responsibilities:
Provide business-centric support of IT systems and platforms in support of our scientific operations and processes.
Develop, implement, troubleshoot and support solutions independently for the digital infrastructure and workflows within QTAS including custom platform/coding solutions, visualization tools, integration of new software/hardware, and analysis and troubleshooting support.
Lead the analysis of large ADME-related datasets , contributing to the understanding and optimization of

drug absorption, distribution, metabolism, and excretion properties
Apply

computational tools and machine learning/deep learning techniques to analyze and interpret complex biological data

relevant to drug discovery.
Develop predictive models and algorithms

for identifying potential drug candidates with desirable ADME properties.
Collaborate with teams across biological sciences and drug discovery to integrate computational insights into practical drug development strategies.
Communicate findings and strategic input to cross-functional teams, including Translational Science, Medicine, and Late Development groups.

Qualifications:
Bachelors or Masters Degree in Data Science, Computer Science, Computational Chemistry, or related relevant discipline typically with 5 to 10 (BS) or 2 to 5 (MS) years related industry experience.
Passion for data analysis, solving technical problems and applying new technologies to further scientific goals.
Strong proficiency in programming (e.g., SQL, Python, R, MATLAB),

database

technologies (Oracle, mySQL, relational databases; graph databases are a plus

machine learning/deep learning (network architectures are a plus),

dimensionality reduction techniques (e.g., PCA), and possible

cheminformatics software suites
Demonstrated

experience in the analysis and visualization of large datasets . Proficiency in any of the following technologies is valued:

Python (including libraries such as Matplotlib, Seaborn, Plotly, Bokeh), JavaScript, Julia, Java/Scala, or R (including Shiny).
Comfortable

working in cloud and high-performance computational environments (e.g., AWS and Oracle Cloud)
Excellent communication skills and ability to work effectively in interdisciplinary teams.
Understanding of pharma R&D process and challenges in drug discovery is preferred.
Proven ability to work in a team environment; ability to work well in a collaborative fast-paced team environment.
Excellent oral and written communication skills and the ability to convey IT related notions to cross-disciplinary scientists.
Thorough theoretical and practical understanding of own scientific discipline
Background and/or experience in the biotechnology, pharmaceutical, biology, or chemistry fields is preferred.

Key Leadership Competencies:
Builds strong relationships with peers and cross-functionally with partners outside of team to enable higher performance.
Learns fast, grasps the 'essence' and can change course quickly where indicated.
Raises the bar and is never satisfied with the status quo.
Creates a learning environment, open to suggestions and experimentation for improvement.
Embraces the ideas of others, nurtures innovation and manages innovation to reality.
by Jobble

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