DATA SCIENTIST
About Nference
At nference, the Google of Biomedicine, we are making the world's biomedical knowledge computable to power quantum leaps in human health. By partnering with elite medical institutions, we transform massive, unstructured datasets-including clinical notes, lab results, and medical images-into a structured, research-ready ecosystem. Leveraging state-of-the-art AI and high-performance computing, we bridge the gap between clinical phenotypes and molecular genotypes to accelerate the discovery of next-generation diagnostics and personalized treatments.
Role Overview
The
Data Scientist will develop and deploy machine learning and deep learning solutions, with a focus on NLP and Large Language Model (LLM) applications for healthcare use cases. The role involves preparing and analyzing data, building and evaluating models, implementing inference pipelines, and collaborating with engineering and product teams to deliver scalable AI solutions. The individual is also expected to follow software engineering best practices, maintain clear technical documentation, and ensure compliance with healthcare data privacy and governance standards.
Requirements
- Bachelor's/Master's in Computer Science, Data Science, or related field.
- 1 - 3 years of experience in data science, machine learning, or applied AI roles.
- Strong proficiency in Python and deep learning frameworks (PyTorch/TensorFlow/scikit-learn).
- Solid understanding of NLP and deep learning concepts, including transformers, embeddings, and LLM fundamentals.
- Hands-on experience with model development workflows, including preprocessing, training, and evaluation.
- Experience working with LLM-based systems (prompting, embeddings, or basic fine-tuning).
- Ability to build basic production components, including REST APIs, and familiarity with Docker and Git.
- Awareness of data privacy and healthcare data considerations (e.g., HIPAA concepts).
Responsibilities
- Develop and train ML/DL models, including LLM-based workflows (prompting, embeddings, basic fine-tuning) for defined use cases.
- Perform data preprocessing, feature engineering, and dataset preparation, particularly for NLP and unstructured healthcare data.
- Execute experiments and evaluations, applying appropriate metrics and analyzing model performance and errors.
- Implement LLM components such as embedding pipelines, retrieval steps, and prompt-based systems under defined architectures.
- Build and maintain model inference components (e.g., REST APIs, batch pipelines) for integrating models into applications.
- Write clean, modular, and version-controlled code, and contribute to containerized workflows (e.g., Docker-based setups).
- Debug and optimize issues across data, model training, and inference stages, ensuring functional correctness and baseline performance.
- Handle and process data in accordance with HIPAA requirements and internal data governance policies, flagging any data quality or compliance concerns to senior team members.
Benefits
- Industry Prestige: Secure a permanent role at the Google of Biomedicine (per The Washington Post), working alongside elite CS, MD, and PhD talent from top-tier universities.
- Unrivaled Data & AI: Deploy cutting-edge ML and analytics against exclusive, large-scale clinical, molecular, and imaging datasets to solve complex biomedical challenges.
- Research & Impact: Drive real-world breakthroughs in drug discovery with opportunities to be credited as a contributing author in high-impact scientific publications.
- Culture & Flexibility: We foster a fast-paced, collaborative environment with a hybrid work model (eligible after a 3-month probation period) to balance innovation with personal well-being.
- Perks & Growth: Enjoy comprehensive reimbursements for gym memberships, tech gadgets, high-speed internet, and professional development courses, along with complimentary breakfast, lunch, and snacks at the office.
