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About Avary.ai Avary.ai is building an AI-native recruiting platform designed to make hiring faster, more efficient, and more consistent. Our platform automates key parts of the recruiting workflow, including resume screening, AI-powered structured interviews, candidate evaluation, interview reporting, and hiring workflow orchestration. As a Data Scientist Intern, you will work with real recruiting and interview data and help us improve the intelligence behind Avary’s candidate evaluation and hiring decision-support systems. What You’ll Work On You will work closely with Avary’s engineering and product teams on practical data science problems across candidate evaluation, recruiting analytics, and AI system quality. Depending on the project, your work may include: Analyze structured and unstructured recruiting data, including resumes, interview responses, candidate evaluation results, and hiring funnel data. Develop metrics and analytical frameworks for measuring candidate quality, interview performance, evaluation consistency, and recruiting efficiency. Analyze AI interviewer and LLM-generated evaluation results to identify patterns, failure cases, bias, and opportunities for improvement. Design experiments to evaluate different prompts, interview strategies, scoring rubrics, and AI evaluation models. Build statistical or machine learning models for problems such as candidate-job matching, candidate ranking, interview outcome analysis, and recruiting funnel prediction. Develop datasets and evaluation pipelines for measuring the quality of LLM-based recruiting systems. Perform exploratory data analysis and communicate findings through dashboards, visualizations, and concise analytical reports. Work with product and engineering teams to turn analytical insights into product improvements. Help establish data quality checks, experiment tracking, and reproducible analytical workflows. Explore new applications of machine learning and generative AI in recruiting and talent intelligence. Example Projects Projects may include: Candidate Evaluation Analysis: Study the relationship between interview answers, AI evaluation scores, recruiter decisions, and hiring outcomes. LLM Evaluation Framework: Build benchmarks and metrics to measure the consistency and quality of AI-generated candidate assessments. Resume–Job Matching: Develop or evaluate models that measure how well a candidate’s experience and skills match a job description. Interview Quality Analytics: Analyze interview questions and candidate responses to determine which signals are most useful for candidate assessment. Recruiting Funnel Analytics: Identify bottlenecks and conversion patterns across application, screening, interview, and hiring stages. Evaluation Calibration: Analyze scoring distributions and help improve the consistency of candidate evaluation across jobs and interview sessions. AI Experimentation: Run controlled experiments comparing prompts, models, evaluation rubrics, or interview strategies. What We’re Looking For Currently pursuing or recently completed a degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, or a related technical field. Strong analytical and problem-solving skills. Experience with Python and common data analysis libraries such as Pandas and NumPy. Working knowledge of SQL and relational data analysis. Understanding of statistics, probability, experimental design, and data visualization. Familiarity with machine learning concepts and common ML workflows. Ability to communicate analytical findings clearly to both technical and non-technical teammates. Comfortable working in a fast-moving startup environment and independently exploring ambiguous problems. Nice to Have Experience with machine learning libraries such as scikit-learn, PyTorch, or TensorFlow. Experience with LLMs, prompt engineering, embeddings, RAG, or AI agents. Familiarity with LLM evaluation techniques, benchmarking, or human/AI evaluation workflows. Experience analyzing text or other unstructured data using NLP techniques. Experience with visualization or BI tools. Familiarity with A/B testing, causal inference, ranking systems, recommendation systems, or information retrieval. Experience working with production datasets, data pipelines, or cloud platforms. What You’ll Learn During the internship, you will gain hands-on experience with: Applying data science to a real AI SaaS product. Evaluating and improving production LLM and AI-agent systems. Designing metrics and experiments for AI product development. Working with real-world recruiting and interview datasets. Translating analytical findings into product and engineering decisions. Building data science workflows in an early-stage AI startup. Collaborating directly with founders and engineers on production-facing projects. Ideal Candidate We are looking for someone who enjoys going beyond simply training a model. You should be interested in understanding why a system behaves the way it does, how to measure whether it is actually working, and how data can be used to improve the product. You do not need to know everything before joining. We value strong fundamentals, curiosity, ownership, and the ability to learn quickly. Originally posted on Himalayas