Do you want to be at the forefront of AI drug discovery and advance the next generation of lifesaving medicine? Now's your opportunity!

BullFrog AI, a technology-enabled drug discovery company, uses AI to aid in advancing the next generation of lifesaving medicine. We are at the intersection of computational biology and Artificial Intelligence and we're seeking an exceptional Senior Vice President of Artificial Intelligence (SVP AI) with a strong background in strategy, leadership, and biomedical analytics to join our expanding team. The selected candidate will have the opportunity to lead cutting-edge projects that integrate AI-driven analytics with groundbreaking biomedical research.

In this role, the SVP of AI will lead the development of a proprietary ensemble AI platform for multi-modal healthcare data. Additionally, you will oversee a team of data scientists whose day-to-day work is client-dependent with flavors of target discovery, patient subtyping, and exploring multimodal and/or mixed data to gain insights. Additionally, they will build and provide technical contributions to the company’s in-house platform.

Additional Roles & Responsibilities

Lead and manage a team that:
Designs, implements, and optimizes, AI models using diverse healthcare data
Engages in multi-modal genomic analyses such as RNA-Seq differential expression, clustering, and GWAS
Implements AI solutions tailored to life sciences datasets
Collaborate effectively with remote teams to ensure seamless communication and delivery, meeting or exceeding expectations on time
Develop algorithms and software solutions that integrate and evaluate large datasets from multiple disparate sources, and collaboratively
Identify biologically and clinically meaningful insights from large data and metadata sources
Interpret and effectively communicate data insights and findings to Key stakeholders with great attention to detail & accuracy
Expand applications across existing and novel Analytics & Big Data domains.
Keep current with technology advances and competitive landscape in the digital health industry
Educate and present complex data science findings to non-technical stakeholders, ensuring clarity and actionable insights

Education Requirements

Bachelor's degree from a top-ranking university in the United States or the United Kingdom.
A PhD in a quantitative field such as biology, computer science, or engineering, from a top-ranking university in the United States or the United Kingdom.

Technical Requirements

A minimum of 10 years of post-graduate experience in life sciences analytics, e.g., in Pharma, Biotech, or Consulting
A deep understanding of the drug discovery landscape
At least 1 year of experience working with large language models
Demonstrated coding proficiency in Python
Experience in leading teams to apply AI tools in a biotech/pharma
setting
Proven record of success in using AI and machine learning tools to
extract untapped value from the biomedical landscape throughout the
process of drug development and clinical testing
Proven experience in developing and implementing AI algorithms for
quantum computing, utilizing quantum systems to achieve enhanced
performance and exponential efficiency in solving complex problems.
Extensive experience in facilitating preclinical development, clinical
testing, and leveraging real-world evidence across broad swaths of
computational topics in drug discovery and development
Knowledge of programming in JAVA, SQL, Python, R, and Scala, and
algorithm development
Experience and a proven record of success in leveraging AI techniques for preclinical drug discovery (e.g. lead compound identification and
target discovery) and/or clinical drug development efforts
Proven problem-solving and decision-making skills to include resource
planning (time allotment, deadlines, timelines, minimum viable solutions, etc.)

Leadership, Work Style, and Communication

Demonstrates strong accountability by setting clear expectations, delivering on commitments, and fostering a culture where both the individual and the team are held responsible for performance and outcomes, in a remote setting
Proactively identifies opportunities and challenges, presenting thoughtful recommendations and actionable solutions to leadership to drive continuous improvement and organizational success
Ability to communicate computational results and outcomes to scientists in both quantitative and non-quantitative disciplines, as well as to external collaborators
Demonstrated ability to conceive and communicate corporate strategy in this context
Experience as a dynamic and resolute team leader and manager, capable of inspiring individual growth and team success.

Desired Skills

Experience working in a public company
Expertise in big data analytics from a data science and data
engineering perspective, to include expertise in data manipulation, visualization, building and optimizing classifiers using machine learning and deep-learning-based techniques in drug discovery and clinical development.
Familiarity with medicinal chemistry and/or high throughput screening is a plus
Familiarity with major healthcare data types such as -omics, claims, and clinical data (EHR).
Prior experience in multi-modal analysis with deep learning.
Causal inference experience is a significant plus.
NLP/LLM expertise applied to life sciences and healthcare text.
Strong foundation in data engineering and databases.
Experience with graphs, including feature engineering, machine learning, and visualizations.
Cloud platform experience, particularly AWS or Google Cloud.
Demonstrated ability to deliver complex data science outputs to senior leadership.

Benefits:

20 days of paid time off annually
11 paid holidays annually
Medical, Dental, and vision coverage with eligibility on first day of
employment
Short-Term Disability
401k with enrollment upon day one
Eligibility for bonus and stock options based on a combination of
individual and company performance.

BullFrog AI

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SVP, Artificial Intelligence (Lifesciences)