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 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.

About the candidate

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.