Applied Data Scientist
Confirmed on the employer's own hiring board on Oct 2, 2026. First seen by Alion on Aug 24, 2026.
About Rhino
Rhino Federated Computing Rhino solves one of the biggest challenges in AI: seamlessly connecting siloed data, apps, and models through federated computing. Our platform enables our customers to share business insight and processing without compromising intellectual property, data privacy, or data security. Our Rhino Federated Computing Platform offers flexible architecture across multi-cloud and on-prem hardware, end-to-end data management controls and workflows, and privacy enhancing technologies. Rhino is trusted by over 60 leading consortia and enterprises worldwide, including 14 of 20 of Newsweek’s ‘Best Smart Hospitals’ and top 20 global biopharma companies.
The company is headquartered in Boston, with an R&D center in Tel Aviv.
About the role
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 and AI agents 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.
A Great Fit For...
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 & Agentic Systems: Proven experience working with LLM APIs (OpenAI, Anthropic, etc.), prompt engineering, building AI agents / agentic workflows, and developing functional end-to-end AI 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.
Bonus:
- Experience with Healthcare, Life Sciences, or Biomedical data.
- 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.
Location: Tel Aviv (Hybrid)