Planet Pharma
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https://www.propharmagroup.com
https://www.propharmagroup.com
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Target PR Range: 60-80/hr
*Depending one experience
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/ML, 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/ML 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, Masters, or Ph.D. in Data Science, Computer Science, Computational Chemistry, or related relevant discipline typically with 8 to 12 (BS), 4 to 8 (MS), or 0 to 4 (Ph.D.) 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.
*CO/NYC candidates might not be considered
Data Scientist III
N Waukegan Rd North Chicago, IL 60064 US
Posted: 02/22/2024
2024-02-22
2024-04-14
Employment Type:
Contract
Job Category: Data Sciences
Job Number: 600439
Is job remote?: Yes
Country: United States
Job Description
Target PR Range: 60-80/hr
*Depending one experience
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/ML, 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/ML 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, Masters, or Ph.D. in Data Science, Computer Science, Computational Chemistry, or related relevant discipline typically with 8 to 12 (BS), 4 to 8 (MS), or 0 to 4 (Ph.D.) 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.
*CO/NYC candidates might not be considered
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