[D] Average Number of Interviews to Get a Job (US)

Hi all,

Do you have a guess of what is the average number of interviews people make until getting a job offer in ML in the US? I made 23 interviews in the last ~8 months without an offer. I don’t know if they find my experience outdated, or if my background is actually okay but they keep constantly choosing someone who worked in a job recently, or if there is a problem in the way I communicate or something else.

Between 2020 and 2023, I worked as a Data Scientist for ~3 years. I put what I did during this period here

• Curated high-quality question–answer pairs from company documents and fine-tuned an LLM (RoBERTa) for extractive question answering. This resulted in a 20% improvement in exact match score.

• Trained, optimized, and evaluated deep learning model to predict whether changes in documents need to be reported. Experimented with MLflow and deployed it as a REST API.

• Fine-tuned a BERT-based sentence transformer and built an NLP pipeline to extract key topics from company documents. Deployed and integrated the model into an application to deliver actionable document insights.

• Designed and implemented end-to-end ETL pipelines with Python, Spark, and SQL to ingest data from different document sources, extract the right data from these documents, and apply various data/text preprocessing methods to ensure data quality, diversity, and compatibility with downstream machine learning models.

• Built, optimized, and deployed a deep learning pipeline to classify the regulatory questions into correct categories and integrated it into an application which saved the department approximately $1,500,000

After 2023, I started my Master of Science program in Computer Science in T20 university in the US. I graduated in May 2025. I did an agentic AI project like this:

• Built a multi-agent data analytics chatbot using GPT-4 and LangGraph to orchestrate specialized LangChain tools for file parsing, automated statistical analysis, anomaly detection, and data visualization.

• Implemented production-ready infrastructure with authentication, session management, file management, caching, and rate limiting.

• Implemented backend API with FastAPI and containerized deployment on AWS EC2 using Docker and Docker Compose.