Sr. AI Scientist - Life Sciences

Abbott

5d•$99,300 - $198,700•Remote

About The Position

The Sr. AI Scientist is a largely self-driven, hands-on role contributing individually and leading projects of extremely complex scope. Working with a team, the position applies advanced artificial intelligence and machine learning algorithms and models to complex biological, genomic, clinical, and healthcare data for advanced cancer screening and precision oncology. The role provides technical leadership and direct contributions throughout solution development, from ideation and design through implementation and deployment. The Sr. AI Scientist helps shape the strategic vision for applying cutting-edge AI methodologies at Abbott Cancer Diagnostics. The position partners with biostatisticians, bioinformatics scientists, and data scientists to support efforts to prevent cancer, detect it earlier, and guide personalized treatment.

Requirements

Ph.D. in Statistics, Computational Biology, Computer Science, or a related quantitative field; or a master’s degree in one of these fields or a related quantitative field plus 4 years of relevant experience.
At least 5 years of experience in statistics, computational biology, applied mathematics, or a related quantitative field.
At least 5 years of experience with artificial intelligence and machine learning algorithms.
Demonstrated mastery of advanced AI concepts, including artificial neural networks, deep learning, and model architectures.
Demonstrated mastery of AI and ML techniques in at least one of natural language processing, image processing and computer vision, or image and pattern recognition.
Demonstrated mastery of large language models for generative AI and related concepts, including transformer architecture and retrieval augmented generation.
Strong programming ability and demonstrated experience with Python and common machine learning libraries such as PyTorch and MLFlow.
Knowledge of and experience working with open-source AI models.
Excellent communication skills and ability to explain complex topics to audiences with varied backgrounds.
Ability to perform the essential duties with or without accommodation.
Authorization to work in the United States without sponsorship.

Nice To Haves

At least 8 years of life sciences industry experience working with biological data.
At least 8 years of industry experience in molecular diagnostics, preferably cancer diagnostics.
Expertise in data-mining approaches in healthcare settings that generate insights from routinely collected healthcare data.
Basic knowledge of ML-Ops and processes for managing machine-learning model versioning and deployment.
Scientific understanding of cancer biology.
Basic knowledge of cloud computing, particularly AWS SageMaker and HyperPod.

Responsibilities

Develop perspectives on what advances in AI technology enable the organization to do more efficiently or effectively.
Work with stakeholders to identify, refine, prioritize, and formalize requirements for AI- and ML-based solutions, particularly for bioinformatics, biostatistics, and data engineering applications.
Collaborate with ML/AI engineers and relevant partners to provide technical and strategic leadership in designing software solutions using state-of-the-art AI and ML techniques.
Lead implementation and deployment of ML and AI solutions with AI/ML and software engineers.
Balance leadership responsibilities with hands-on work and clear deliverables.
Help develop an AI strategy that enables the team to use existing and in-house AI and ML tools to improve bioinformatics, biostatistics, and data science processes and outcomes.
Lead initiatives to build training resources and knowledge bases and educate others about advanced AI and ML technologies and best practices.
Help the team solve complex problems through analytical judgment and innovative solutions.
Mentor and coach junior team members.
Serve as a resource and subject matter expert in core and cross-functional meetings.
Contribute innovative ideas and methods to the intellectual property portfolio.
Select methods, techniques, and evaluation criteria and interpret results within broadly defined practices and policies.
Communicate complex or sensitive information effectively and influence decision-making among audiences of peers.
Uphold company mission and values, support Quality Management System policies and procedures, maintain regular and reliable attendance, and model an inclusion mindset.
Work the designated schedule and use a mobile device, tablet, or computer and/or type for approximately 85% of a typical working day.
Travel 5% of working time away from the work location; travel may include overnight or weekend trips.