Intern - Data Scientist
Location: Bangalore, India
About nference
nference is an AI-powered technology company transforming biomedical research by leveraging large-scale data, advanced machine learning, and deep scientific expertise. Our mission is to accelerate breakthroughs in healthcare and life sciences by building innovative products that help researchers and clinicians solve complex real-world problems.
Our People
At nference, you'll work alongside software engineers, AI researchers, physicians, scientists, and product leaders who are passionate about solving meaningful, real-world problems.
We foster a culture of ownership, curiosity, collaboration, technical excellence, and continuous learning. Our teams are encouraged to ask questions, share ideas, learn from experienced colleagues, and contribute to impactful projects.
The Opportunity
We're looking for a motivated Data Scientist Intern with a strong foundation in Python, statistics, and data analysis. This internship offers an opportunity to work with complex healthcare data, develop data processing workflows, and explore practical applications of machine learning and large language models (LLMs).
You will collaborate with experienced data scientists and engineers to prepare and analyze electronic health record (EHR) data, build data pipelines, validate data quality, and support LLM-assisted information extraction and cohort refinement. This role is suited for someone who enjoys solving data-intensive problems, working with messy real-world datasets, and applying statistical reasoning to derive reliable insights.
What You'll Do
- Develop data pipelines to fetch, aggregate, clean, and preprocess raw patient data from electronic health records (EHRs).
- Apply statistical methods to understand data distributions, normalize variables, handle variations, detect anomalies, and assess data quality and integrity.
- Define, segment, and statistically validate data cohorts for analysis and research.
- Build basic batch-processing pipelines and automate data transformation workflows.
- Support LLM-assisted workflows using prompting, structured outputs, embeddings, and retrieval techniques for information extraction and cohort refinement.
- Conduct experiments, analyze results, and evaluate data and inference pipeline performance.
- Debug and optimize data processing and inference workflows.
- Use Git for version control and maintain reproducible data workflows.
- Document data cleaning rules, cohort definitions, analytical methods, and results to support reproducibility.
- Follow data privacy, security, and compliance requirements when handling healthcare data.
What We're Looking For
Required Qualifications
- Pursuing or recently completed a Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Economics, or a related quantitative field.
- Strong programming skills in Python, with practical knowledge of Pandas and NumPy for data manipulation and analysis.
- Solid understanding of statistical fundamentals, including data distributions, normalization, hypothesis testing, and anomaly detection.
- Strong analytical skills, with the ability to define, segment, and validate data cohorts using appropriate statistical methods.
- Understanding of machine learning and natural language processing (NLP) fundamentals.
- Familiarity with scikit-learn, SQL for data retrieval, and Git.
- Ability to work with messy, inconsistent, and unstructured datasets with strong attention to detail.
- Good problem-solving, documentation, and communication skills.
- Willingness to learn new tools and apply analytical techniques to real-world healthcare problems.
Preferred Qualifications
- Academic or personal projects involving data engineering, statistical data analysis, or cohort building.
- Exposure to LLM applications, retrieval-augmented generation (RAG), embeddings, or vector databases.
- Familiarity with LLM prompting and structured outputs for data extraction or analysis.
- Awareness of healthcare data privacy concepts and regulations, such as HIPAA.
What You'll Gain
- Hands-on Experience: Work with real-world healthcare data and practical data science workflows.
- Technical Learning: Gain exposure to machine learning, statistical analysis, data pipelines, and LLM applications.
- Mentorship: Learn from experienced data scientists and engineers.
- Meaningful Impact: Contribute to AI-powered solutions that support biomedical research and healthcare innovation.
- Career Development: Strengthen your analytical and technical skills in a collaborative, research-driven environment.
Equal Opportunity Employer
nference is committed to building a diverse, equitable, and inclusive workplace where everyone has the opportunity to thrive. We welcome applicants from all backgrounds who are passionate about learning, innovation, and building technology that transforms healthcare.