Senior AI Scientist, Life Sciences

Abbott

Confirmed live yesterday High trust
Remote

Quick summary

Work type
Remote
Location
Remote
Salary
$99,300–$198,700 / yr
Employment
Full-time
Posted
87 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $202k
This role $149k
$82k $259k
below market most similar roles pay here above market

This role pays less than 87% of similar roles. Most pay $162,000–$242,312 — the blue band above. At the midpoint, this role pays about $149k versus about $202k for comparable roles.

Based on 240 similar postings.

Employer

About Abbott

Abbott Laboratories is a global healthcare company that manufactures and markets a broad and diversified line of health care products including diagnostics, medical devices, nutritionals, and branded generic pharmaceuticals. Industry: Healthcare & Medical Devices

Abbott currently has 193 open roles on FindRole.

Listed pay typically runs $99,300–$198,700 across 123 roles with salary data.

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View all roles at Abbott

At a glance

TL;DR · Senior AI Scientist, Life Sciences

JOB TITLE: Sr. AI Scientist - Life Sciences The Sr. AI Scientist joins the ONCO Cancer Diagnostics team to lead the development of advanced artificial intelligence and machine learning solutions. This hands-on role involves providing technical leadership throughout the solution development process, from initial ideation and design to implementation and deployment. The successful candidate will collaborate with biostatisticians and bioinformatics scientists to solve complex problems involving biological, genomic, clinical, and healthcare data within the context of advanced cancer screening and precision oncology. Key responsibilities include crafting a strategic vision for AI methodologies, mentoring junior staff, and building training resources. Required technical skills include mastery of artificial neural networks, deep learning, large language models, and transformer architecture. Proficiency in Python, PyTorch, MLFlow, and open-source models is essential for managing these sophisticated analytical tasks.

What you'll do

  • Apply advanced AI and machine learning algorithms to solve complex problems involving biological, genomic, and clinical healthcare data.
  • Lead the design, implementation, and deployment of software solutions utilizing state-of-the-art artificial intelligence techniques.
  • Identify, refine, and prioritize requirements for AI-based solutions focusing on bioinformatics, biostatistics, and data engineering.
  • Develop and execute an AI strategy to leverage existing and in-house tools for accelerating data science processes.
  • Create training resources and knowledge bases to educate the team on advanced AI technologies and best practices.
  • Contribute to the intellectual property portfolio by implementing innovative ideas to improve current methods or solve problems.
  • Provide technical leadership and mentorship to junior team members on complex AI projects.
  • Communicate complex technical information and influence decision-making among peers and cross-functional stakeholders.

What we're looking for

  • Ph.D. in Statistics, Computational Biology, Computer Science, or a related quantitative field.
  • Master’s Degree in Statistics, Computational Biology, Computer Science, or a related quantitative field plus 4 years of relevant experience.
  • 5+ years of experience in statistics, computational biology, applied mathematics, or a related quantitative field.
  • 5+ years of experience with artificial intelligence and machine learning algorithms.
  • Mastery of advanced AI concepts including artificial neural networks, deep learning, and model architectures.
  • Mastery of AI techniques in natural language processing, image processing, computer vision, or pattern recognition.
  • Mastery of large language models for generative AI, including transformer architecture and retrieval augmented generation.
  • Strong programming ability in Python with experience in PyTorch, MLFlow, and open-source AI models.
  • 8+ years of life sciences industry experience working with biological data (preferred).
  • 8+ years of industry experience in molecular diagnostics, preferably cancer diagnostics (preferred).
  • Expertise in data mining approaches within healthcare settings (preferred).
  • Basic knowledge of ML-Ops, cloud computing (AWS SageMaker and HyperPod), and scientific understanding of cancer biology (preferred).

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