Sr Scientist I, Automation Development

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 at www.abbvie.com. Follow @abbvie on LinkedIn, Facebook, Instagram, X and YouTube.

Job Description

The Molecular Profiling and Drug Delivery (MPDD) function within the Small Molecule CMC organization is accountable for a broad range of deliverables across various stages of drug discovery and development. During lead optimization and through candidate selection, MPDD scientists utilize state of the art automation and computational tools supported by expertise in biopharmaceutics, drug delivery, and solid-state chemistry to collaboratively progress candidates with higher probability of success into development and advise clinical formulation strategy. From candidate selection through clinical proof of concept and product launch, MPDD scientists work in cross functional teams to identify the commercial solid form of the active pharmaceutical ingredient (API) and establish structure-property-performance correlations to help deliver robust commercial processes and align control strategies across drug substance and product. They also transition drug substance isolation processes and relevant physical characterization methods to commercial manufacturing sites and work within teams to ensure successful regulatory submissions.

Drug Delivery scientists within AbbVie's MPDD organization work collaboratively with other functions within Development Sciences and Discovery to conduct developability assessment to select molecules with higher probability of success for clinical development and eventual commercialization. This includes profiling of physicochemical properties, development of preclinical formulations, identifying solutions to in vivo absorption challenges, and advancement of novel drug delivery strategies across multiple routes of administration that are aligned with program target product profiles and downstream partners. Modalities of interest include traditional small molecules as well as complex chemically synthesized modalities such as PROTACs and peptides. In this role, you will work in cross-functional teams in matrixed environments and engage in multidisciplinary problem solving.

AbbVie’s MPDD organization is seeking a highly motivated, creative, and collaborative scientist with expertise in chemical automation, assay development, and integrated workflow design for a Senior Scientist I position. The primary role of this position is to develop and implement automated and data-enabled workflows that accelerate decision-making and improve throughput in early development sciences. This individual will contribute to the profiling of physiochemical properties, screening and development of preclinical formulations, and the advancement of platform workflows for emerging modalities of interest. This role will work closely with cross-functional partners across discovery and development sciences to build and optimize integrated capabilities that support faster, data-driven, more efficient pipeline progression. The successful candidate will have strong hands-on experience in assay development, automation, and the generation and interpretation of physicochemical data to inform developability. Experience with preclinical formulation strategies, workflow optimization, and enabling technologies for novel or complex modalities is highly desirable.

Level and compensation will be commensurate with experience.

Qualifications

• Senior Scientist I typically requires Bachelors, Masters, or Ph.D. with 10+ (B.S.), 8+ (M.S.), and 0-3 (Ph.D.) years of experience in pharmaceutical or related industry. Preferred educational backgrounds include but are not restricted to Pharmaceutical Sciences, Pharmaceutical Engineering, Chemical/ Biomedical Engineering, Chemistry and Materials Science.
• Familiarity and preferred hands-on experience in physicochemical property assessment related to understanding CMC developability profile and potential risk mitigation strategies. Experimental and automation techniques of interest include but are not limited to: plate-based optical spectroscopy, liquid handler operation, custom scientific programming and scripting, machine learning and model-building concepts, high-performance liquid chromatography (HPLC), mass spectrometry, powder X-ray diffraction (pXRD), optical microscopy, light scattering techniques, thermal analysis, and other biophysical characterization techniques. Experience with automated data capture, laboratory instrumentation integration, assay miniaturization, and data analysis workflows is desirable.
• Hands-on experience in assay development, automation, and workflow optimization, with an ability to design and implement efficient, reproducible, and scalable laboratory processes.
• Strong understanding of physiochemical property profiling and its application to early developability assessment, including methods relevant to solubility, stability, and other compound attributes that inform pipeline decisions.
• Familiarity with preclinical formulation development and screening strategies, including support for enabling formulations for emerging or complex modalities.
• Familiarity with analytical, biophysical, and in vitro techniques used to generate high-quality data for early development decision making.
• Demonstrated ability to generate, structure, and capture data in a consistent and traceable manner to support predictive modeling and AI/ML applications.
• Experience collaborating with data scientists, computational chemists, or informatics partners.
• Excellent organizational skills, self-management, and attention to detail, with the ability to manage multiple priorities in a fast-paced environment.

Key Competencies

• Builds strong relationships with peers and cross-functional partners to enable higher performance.
• Learns fast, grasps the