Company Description

About AbbVie

AbbVie'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 AbbVie, please visit us atwww.abbvie.com. Follow @abbvie onLinkedIn,Facebook,Instagram,XandYouTube.

Job Description

While the AI innovation race in Biopharma is focused on Drug discovery, Product Development/ CMC represents the next barrier/ bottleneck. The complexity of biological systems, the rigor of regulatory expectations, the pace of pipeline growth, and the enormous value at stake make this one of the highest-leverage domains for applied data science and AI in the entire pharmaceutical value chain.

We here at BTS - PDST, are building a dedicated, AI-native team that is driving cutting edge programs across early stage, late stage and commercial product development to accelerate E2E product development and launch, maximize yields of block buster products. Through our deep collaboration with PDST scientists we are boldly reimagining how AbbVie can bring our pipeline products and lifesaving drugs to patients faster, safer and in cost effective manner fueled by AI.

Early-Stage Biologics Data Scientist is a senior individual contributor role built for a scientist-engineer who thinks in systems, builds with purpose, and leads through technical credibility. This role is a shaper of outcomes. You will embed AI and advanced analytics directly into AbbVie's early-stage biologicspipelinesincluding process characterization studies, process robustness, and control strategy development. You will architect data solutions, build and deploy predictive models, andestablish the analytical foundation that enables AbbVie to make faster, smarter, more defensible decisions at every stage of commercial biologics development.

• Enterprise-scale scope: Enterprise-scale biologics portfolio spanning clinical, commercial, and lifecycle stages.

• Building AI playbook for the future: First-in-AbbVie and first-in-biologics analytical approaches; you build the AI playbook for the future.

• Growth and Impact: Direct impact on regulatory submissions, commercial readiness, and manufacturing decisions through deep cross-functional exposure to manufacturing, quality, regulatory, and scientific leadership

• Mission: Every model you build helps ensure safe, reliable medicines reach patients at scale

Responsibilities:

Modeling & Predictive Analytics

• Develop, validate, and deploy predictive models that support process development decisions in early-stage biologics, including upstream bioprocess performance, downstream purification behavior, and critical quality attribute (CQA) outcomes.

• Design and apply hybrid modeling approaches combining first-principles process understanding with data-driven techniques to maximize predictive power while maintaining scientific interpretability.

• Build and evolve analytical frameworks that support digital twin concepts and in-silico process optimization, enabling smarter experimental strategies and accelerated development timelines.

Data Strategy & Architecture

• Partner with process scientists, analytical scientists, and engineers to define data strategies for new programs including what data to collect, how to structure it, and how to connect it across experimental campaigns.

• Identify and address data quality, integration, and accessibility challenges that limit the value of existing datasets; advocate for and help implement improved data infrastructure within the pod.

• Ensure that models, analyses, and data assets are built in a manner consistent withGxPprinciples and regulatory expectations, with appropriate documentation and traceability.

Intelligent Workflow Design

• Function as a solution architect for analytical and AI-driven workflows: select the right approach for each problem whether that means classical statistical methods, supervisedor unsupervised machine learning, retrieval-augmented generation, multi-step orchestrated AI pipelines, or hybrid mechanisms based on scientific need, data availability, and interpretability requirements.

• Identify opportunities to automate, accelerate, or augment scientific workflows using intelligent tooling, and take ownership of scoping, building, and validating those solutions.

• Stay current with the rapidly evolving landscape of AI and machine learning methods; evaluate emerging approaches for applicability to biologics development contexts.

Experimental Design & Decision Support

• Collaborate with process development scientists to design experiments that are statistically rigorous, resource-efficient, and maximally informative including design of experiments (DoE), Bayesian optimization, and active learning strategies.

• Support process characterization studies by providing statistical analysis, model-based risk assessment, and data-driven identification of critical process parameters (CPPs) and their relationships to CQAs.

• Contribute to technology transfer readiness by developing robust analytical frameworks that translate process understanding into transferable, defensible process knowledge.

Stakeholder Engagement & Scientific Communication

• Translate complex quantitative analyses into clear, actionable insights for scientists, engineers, and senior leaders bridging technical depth with business relevance.

• Actively participate in cross-functional discussions, contributing data-driven perspective to program decisions, risk assessments, and development strategies.

• Document, present, and defend analytical work in a manner appropriate for internal technical reviews and regulatory submissions.

Qualifications

Required:

• Bachelors degree in computer science or a related discipline with 7 years experience, Master's Degree with 6 years experience, or PhD with 2 years experiencein IT, application program development

• Respective years of hands-on experiencebuilding and deploying data science or machine learning solutions in a scientific or engineering-intensive environment.

• Expert-level Python proficiency; deep familiarity with the scientific Python ecosystem (NumPy, pandas, scikit-learn,PyTorchor TensorFlow,modern data engineering (cloud, big data, pipeline orchestration)

• Strong foundation inbusiness analytics, with mastery of tools such as R, Dataiku, AWS SageMaker, Spark, Tableau

• Experience withdesign ofexperiments (DoE) methodologies, Bayesian methods, or active learning in scientific applications.

• Familiarity with knowledge graph, retrieval-augmented, or orchestrated AI/LLM-based systems applied to scientific or technical domains.

• Experience applying data science in aGxP-regulated environment, with working knowledge of FDA/EMA expectations for process validation, continued process verification (CPV), and control strategy.

• Familiarity withMLOpsprinciples, model lifecycle management, or deployment of analytical tools in regulated or enterprise environments.

• Ownership orientation: you define your own problem space, drive solutions to completion, and hold yourself accountable to outcomes not just outputs.

• Solution-architect instinct: you think before you build, consider the full landscape of available approaches, and choose tools based on fit-for-purpose reasoning rather than familiarity or trend.

• Scientific integrity: you build models you can explain, defend, and improve and you apply the same standard to the work of others.

• Influence through credibility: you earn the confidence of scientists, engineers, and quality professionals by being right, being clear, and being useful not by title or volume.

• Bias for impact: you are drawn to problems where the stakes are high and the analytical opportunity is real, and you are energized rather than intimidated by ambiguity.

Preferred:

• Advanced degree (M.S. or Ph.D.) in Data Science, Biostatistics, Chemical or Biochemical Engineering, Computational Biology, or a closely related quantitative discipline preferred.

• 5+ years of hands-on experiencebuilding and deploying data science or machine learning solutions in a scientific or engineering-intensive environment.

• Experiencein working withdata from diverselab andmanufacturing systems (LIMS, MES,DeltaV/historian,eBRplatforms) and building scalable data pipelines for process analytics.

• Direct experience in biologics or bioprocess development (e.g., cell culture, fermentation, chromatography, filtration, orformulation)either in an industrial or academic research setting.

• Familiarity with CMC development concepts, including process characterization, scale-up, technology transfer, or regulatory filing support (BLA/IND).

• Familiarity with biopharmaceutical data types including bioreactor process data, chromatography profiles, analytical assay results, and spectroscopic measurements

• Familiarity with technology transfer workflows, process characterization study design, or commercial process validation (PPQ/PV) in a biologics or pharmaceutical context.

• Track record of scientific communication publications, regulatory submissions, technical reports, or equivalent that demonstrates the ability to convey complex analytical work clearly and credibly.

Additional Information

Applicable only to applicants applying to a position in any location with pay disclosure requirements under state orlocal law:

• The compensation range described below is the range of possible base pay compensation that the Companybelieves ingood faith it will pay for this role at the timeof this posting based on the job grade for this position.Individualcompensation paid within this range will depend on many factors including geographic location, andwemayultimatelypaymore or less than the posted range. This range may bemodifiedin thefuture.

• We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick),medical/dental/visioninsurance and 401(k) to eligibleemployees.

• This job is eligible toparticipatein our long-term incentiveprograms.

Note: No amount of payis considered to bewages or compensation until such amount is earned, vested, anddeterminable.The amount and availability of any bonus,commission, incentive, benefits, or any other form ofcompensation and benefitsthat are allocable to a particular employeeremainsin the Company's sole andabsolutediscretion unless and until paid andmay bemodifiedat the Companys sole and absolute discretion, consistent withapplicable law.

AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled.

US & Puerto Rico only - to learn more, visithttps://www.abbvie.com/join-us/equal-employment-opportunity-employer.html

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https://www.abbvie.com/join-us/reasonable-accommodations.html