Applied Scientist

About RISA Labs

Cancer patients face not just a disease, but a broken system where delays in treatment are common due to outdated, error-prone workflows. RISA Labs is changing that with our Business Operating System as a Service (BOSS) - a powerful platform built for the vertical complexity of healthcare, that transforms healthcare operations. Unlike simple automation tools, BOSS breaks down complex workflows into small tasks, managed by smart AI agents like LLMs, digital twins, and reinforcement learners. This creates a digital workforce that doubles the efficiency of healthcare teams, letting a 1,000-person institution operate like it has 2,000 staff overnight.

Founders

RISA was founded by Kshitij Jaggi and Kumar Shivang, IIT Kanpur alumni with a proven track record from their previous healthcare startup, Urban Health. Their vision is to streamline oncology care through cutting-edge technology.

Funding

RISA Labs is backed by $3.5 million in seed funding, led by Flipkart co-founder Binny Bansal, with support from Oncology Ventures, General Catalyst, z21 Ventures, Odd Bird VC, and angel investor Ashish Gupta.

Role Overview 

We’re seeking an Applied Scientist to help us develop and optimize advanced machine learning models, algorithms, and AI solutions that will power our platform. In this role, you’ll work closely with data scientists, engineers, and product teams to implement state-of-the-art AI and machine learning technologies that improve service delivery, enhance decision-making, and automate workflows across industries.

This is an exciting opportunity to work on impactful, high-performance systems in a fast-paced, innovative environment. 

Responsibilities

• Model Development: Design, implement, and optimize machine learning models, including but not limited to, predictive models, recommendation systems, natural language processing (NLP), and computer vision.
• Algorithm Design: Develop and apply advanced algorithms to solve real-world problems, ensuring they are scalable, efficient, and robust.
• Research & Innovation: Stay up-to-date with the latest AI research and apply new techniques to improve the performance and capabilities of our products.
• Collaboration: Work closely with cross-functional teams (engineering, product, operations) to integrate machine learning models into production environments.
• Data Analysis: Analyze large, complex datasets to extract insights and improve model performance.
• Product Impact: Collaborate with product teams to ensure that AI models and solutions align with user needs, ensuring high-quality, reliable output.
• Model Validation & Testing: Ensure the quality of models through rigorous validation, performance testing, and continual refinement based on real-world data and feedback.

Qualifications 

• 3+ years of experience in applied machine learning, data science, or a related field
• Strong proficiency in Python and experience with machine learning libraries such as TensorFlow, PyTorch, or scikit-learn
• Experience with large-scale data processing tools (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Google Cloud)
• Experience with NLP, computer vision, or deep learning is a plus
• Strong analytical skills and experience with data preprocessing, model evaluation, and performance tuning
• Knowledge of statistical methods and data analysis techniques
• Ability to work in a collaborative, cross-functional environment
• Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders
• PhD or Master’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience)

Why You'll Love Working at RISA

This is a hands-on role for builders who love tackling tough problems in healthcare. You’ll work closely with engineers, designers, and oncology experts, getting involved in every step. If you’re driven to create from scratch, grow fast, and make a real impact on cancer care, RISA is the place. With a small team and big vision, your work will shape the future of healthcare efficiency.