Data Scientist Intern
Avary.ai
WorldwideremotePosted 11 days ago
Skill Required
Data-Scientist-InternData-Science-InternData-Analyst-InternMachine-Learning-InternAnalytics-InternAI-Data-Science-InternData-Science-InternshipsData-ScientistData-ScienceData ScientistGenerative AIMachine LearningScikit-learnStatisticsEngineeringTensorFlowanalyticaldesigninganalyticsbuildingPyTorchTestNGPythonPandasdesignNumPyCloudNLPRAGSQLandAIInternship
Key highlights
- Level Required: Currently pursuing or recently completed a technical degree
- Key benefit: Direct collaboration with founders and engineers on production projects
- Notable requirement: Proficiency in Python (Pandas/NumPy) and SQL
Role overview
Avary.ai is building an AI-native recruiting platform designed to make hiring faster, more efficient, and more consistent. Their 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 improve the intelligence behind Avary’s candidate evaluation and hiring decision-support systems, working closely with engineering and product teams on practical data science problems across candidate evaluation, recruiting analytics, and AI system quality.
Responsibilities
- 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.
- 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.
Requirements
- 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.
Benefits
- Gain hands-on experience applying data science to a real AI SaaS product.
- Gain experience evaluating and improving production LLM and AI-agent systems.
- Gain experience designing metrics and experiments for AI product development.
- Gain experience working with real-world recruiting and interview datasets.
- Gain experience translating analytical findings into product and engineering decisions.
- Gain experience building data science workflows in an early-stage AI startup.
- Gain experience collaborating directly with founders and engineers on production-facing projects.
Additional details
- 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