Data Scientist

Company Description

About Abb Vie

Abb Vie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about Abb Vie, please visit us at  Follow @abbvie on Linked In, ()  Facebook, Instagram () , X ()  and You Tube.

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Job Description

This is an exciting opportunity to work on a cross-functional team preparing data and enabling modeling activities in support of Product Development and Manufacturing Sciences organizations at Abb Vie.

Within the Product Development laboratories, the objective is to partner with modeling SMEs and the Digital organization to prepare data from source systems and data lakes, and enable model execution for statistical, hybrid and mechanistic models, with the ultimate objective to streamline and automate the process from data collection through model execution in a "Lab-in-the-Loop" environment.  This environment should enable end-to-end automation of laboratory data and modeling in a way that is accessible for all laboratory scientists to access.

It should also provide an opportunity to gain a deep understanding of bioprocess data, and also to learn multivariate, hybrid and machine learning modeling in support of deeper scientific insights, and upholding the highest standards of quality and productivity.

Responsibilities:

+ Partner with Manufacturing Sciences, the objective is to compile, clean and feature engineer the same laboratory and commercial data sets for multivariate and hybrid "Digital Twin" process models,

+ Partner with the Digital organization to deliver end-to-end data pipelines, products, and models from successful proofs of concept.

+ Routinely demonstrate scientific initiative and creativity in research or development activities.

+ Highly autonomous and productive in performing laboratory research or method development, requiring only minimal direction from or interaction with supervisor.

+ Formulate conclusions and design follow-on experiments based on multidisciplinary data.

+ May initiate new areas of investigation that are scientifically meaningful, reliable, and can be incorporated directly into a research or development program.

+ A primary author of publications, presentations, regulatory documents and/or primary inventor of patents.

+ Demonstrates high proficiency across a wide range of relevant technologies.

+ Understand and adhere to corporate standards regarding code of conduct, safety, appropriate handling of materials, controlled drug and radioactive compounds, GxP compliance, and animal care where applicable.

Qualifications

+ Bachelor's Degree or equivalent education and typically 10 years of experience, Master's Degree or equivalent education and typically 8 years of experience, PhD and no experience necessary.

+ Possess thorough theoretical and practical understanding of own scientific discipline.

Preferred Qualifications:

+ Masters in Data Engineering, Data Science or related

+ Prior experience in biotech or pharmaceuticals preferred

+ Strong experience in programming, including Python, SQL, etc.

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Experience with standard cloud-based data lake infrastructure

+ Experience using source code management tools (e.g. Git Hub)

+ Familiarity with data pipeline tools such as KNime, dbt, etc.

+ Familiarity with model management and pipeline tools preferred (ML Flow, Data Bricks, etc.)

+ Familiarity or willingness to learn Service Now and support application owner and access review for data and modeling capabilities

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Experience with biotech data systems preferred: AVEVA PI, LIMS, ELN, MES, ERP, etc.

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Experience with laboratory automation and data

+ Some experience with BI tools preferred (e.g. Spotfire, PowerBI, etc.)

+ Experience in SIMCA, JMP, or hybrid modeling preferred

+ Interest in learning, or experience with process, statistical, hybrid, mechanistic, and/or machine learning models.

Additional Information

Applicable only to applicants…