AI Data Scientist

We are seeking an AI data scientist to join our data analysis team and develop and operate Anima’s AI analysis pipelines.

Anima Biotech built the Biology Runtime Layer for AI - the Biology GPU - enabling AI models to visually compute inside cells and reason experimentally in biological pathways.

 The Biology GPU unlocks breakthrough applications across the R&D value chain - from discovering pathway signatures and identifying novel targets, to optimizing compounds based on pathway activity and supporting preclinical decisions through seeing biology inside cells.

 Our AI-to-cell stack powers use cases across the discovery process in over 20 programs and strategic collaborations with AbbVie, Takeda, and Lilly.

Key responsibilities:

• Develop and maintain the state-of-the-art Anima imaging AI models.

• Participate in the development of end-to-end data pipelines from raw images to structured datasets at scale, including preprocessing, segmentation, inference, and model scoring.

• Perform advanced analyses such as classification, clustering, anomaly detection, and other methods to derive actionable insights from complex imaging datasets.

• Participate in the development of Anima’s proprietary knowledge LLM.

• Collaborate with internal and external teams, including biologists and domain experts, to integrate AI and classical data analysis solutions.

• Identify and recommend new opportunities to expand and deepen AI adoption across the organization.

• Maintain documentation, coding best practices, and reproducibility of pipelines and models.

• Stay current with emerging trends in AI, deep learning, and imaging technologies.

Education, experience, and other requirements:

• MSc or PhD in Computer Science, Computer Engineering, or a related field, with foundational coursework in biology.

• 1-2 years of experience developing AI solutions, ideally in the domain of biological or medical imaging.

• Proven experience applying deep learning to extract insights from images.

• Experience with self-supervised or advanced generative models.

• Experience working with LLM, fine-tuning, RAG, etc. 

• Familiarity with cloud platforms such as AWS SageMaker, familiarity working with relational databases.

• Solid understanding of probability, statistics, and experimental design.

• Experience with object-oriented and functional programming, version control (Git), and software engineering best practices.

• Detail-oriented, organized, and able to manage multiple priorities effectively.

• Excellent communication skills, capable of explaining complex technical results to diverse audiences.