Rhino solves one of the biggest challenges in AI: seamlessly connecting siloed data through federated computing. The Rhino Federated Computing Platform (Rhino FCP) serves as the ‘data collaboration tech stack’, extending from providing computing resources to data preparation & discoverability, to model development & monitoring - all in a secure, privacy preserving environment.

To do this, Rhino FCP offers flexible architecture (multi-cloud and on-prem hardware), end-to-end data management workflows (multimodal data, schema definition, harmonization, and visualization), privacy enhancing technologies (e.g., differential privacy), and allows for the secure deployment of custom code & 3rd party applications via persistent data pipelines.

Rhino is trusted by >60 leading organizations worldwide - including 14 of 20 of Newsweek’s ‘Best Smart Hospitals’ and top 20 global biopharma companies - and is leveraging this foundation for financial services, ecommerce, and beyond. The company is headquartered in Boston, with an R&D center in Tel Aviv.

We are looking for an Applied Data Scientist to join our growing R&D team. You will play a key role in developing the AI capabilities that power our platform, while also acting as a hands-on practitioner who tests and validates our technology across diverse use cases.

In this role, you will balance the immediate needs of a fast-growing startup with long-term data science tasks. You will be responsible for building internal tools that automate complex data workflows, as well as developing and fine-tuning models that demonstrate the full potential of federated computing.

You will work with a wide range of technologies - from integrating off-the-shelf LLM APIs to fine-tuning State-of-the-Art deep learning models - and collaborate closely with Product and Engineering to improve the platform based on your hands-on experience.

Day-to-day responsibilities:

• Develop Internal AI Engines: Research and implement intelligent tools to automate data mapping, harmonization, and user assistance pipelines using Generative AI and LLMs.
• End-to-End Model Execution: Take ownership of diverse modeling tasks (NLP, Computer Vision, Tabular) from data collection and preparation to training, fine-tuning, and validation.
• Platform Validation & "Customer Zero": Stress-test the Rhino platform by implementing various ML workflows (both federated and centralized) to ensure robustness and identify gaps before they reach the customer.
• Support & Innovation: Assist in solving complex data science challenges while simultaneously researching new methods to enhance our core technology.
• Product Collaboration: Provide feedback to the product team on UI/UX and feature requirements based on your deep technical usage of the system.

This role is for a fast learner who loves technology and is capable of executing quickly without losing sight of the bigger picture. We are looking for a versatile data scientist who can choose the right tool for the job - whether it’s prompt engineering for an LLM, statistical modeling, or training a deep neural network.

Requirements:

• 4+ years of professional experience in Data Science or Applied Machine Learning.
• Strong proficiency in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).
• Generative AI & LLM Expertise: Proven experience working with LLM APIs (OpenAI, Anthropic, etc.), prompt engineering, and building functional AI-driven pipelines.
• Strong software practices within Data/ML workflows: including clean code structure, modular design, reproducibility, and the ability to transition exploratory work into well-organized, maintainable code.
• Adaptability & Versatility: Ability to switch contexts between different domains (NLP, Image Processing, Structured Data) and tasks.
• Model Lifecycle Knowledge: Experience with data curation, model fine-tuning, and rigorous evaluation.
• Startup Mindset: Ability to prioritize effectively in a dynamic environment, balancing "quick wins" for delivery with robust development for the long term.
• Creative Problem Solving: Demonstrated ability to find innovative solutions to complex data or modeling constraints.

Advantages:

• Experience working in a startup environment.
• Experience with Federated Learning.
• Experience with cloud environments (AWS/GCP) and containerization (Docker/Kubernetes).
• Experience in developing internal developer tools or automation products.