Data Scientist

Who We Are

DoubleVerify is an Israeli-founded big data analytics company (Stock: NYSE: DV). We track and analyze tens of billions of ads every day for the biggest brands in the world.

We operate at a massive scale, handling over 100B events per day and over 1M RPS at peak, we process events in real-time at low latencies (ms) and analyze over 2.5M video years every day. We verify that all ads are fraud free, appear next to appropriate content, appear to people in the right geography and measure the viewability and user's engagement throughout the ad's lifecycle.

We are global, with HQ in NYC and R&D centers in Tel Aviv, New York, Finland, Berlin, Belgium and San Diego. We work in a fast-paced environment and have a lot of challenges to solve. If you like to work in a huge scale environment and want to help us build products that have a huge impact on the industry, and the web - then your place is with us.

What you will do

• Develop and improve models that analyze millions of videos and images every day across the web, Smart TVs, YouTube, Facebook, Twitter, Instagram, TikTok and more to detect the content categories they are about.

• Work alongside experienced data scientists and engineers in a cutting-edge environment, contributing to advanced ensembles of models at huge scale.

• Participate in the full development cycle — from research and experimentation through to deployment — gaining hands-on experience with production ML systems.

• Leverage AI-powered tools and assistants as part of your daily workflow to accelerate research, coding, and analysis.

• Help advertisers avoid negative or irrelevant content and target what's most relevant to their brand, keeping the web free for everyone.

Who you are

• B.Sc. in Computer Science, Computer Engineering, Electrical Engineering, Physics, or a related technical field from a leading institution (or equivalent experience). M.Sc. is a plus.

• 0-2 years of industry experience (strong academic projects or internships count a must).

• Strong data science fundamentals you understand experiment design, hypothesis testing, evaluation methodology, bias-variance tradeoffs, feature engineering, and know how to rigorously validate results. You think in terms of precision, recall, and data distributions, not just accuracy.

• Familiarity with Computer Vision concepts (image classification, object detection, or similar) or NLP concepts (text classification, embeddings, language models).

• Comfortable coding in Python with hands-on experience in at least some of: PyTorch, TensorFlow, scikit-learn, OpenCV.

• Comfortable using AI tools in day-to-day work — you see LLMs, code assistants, and AI-powered workflows as force multipliers and already integrate them into how you code, explore data, and solve problems.

• Eager to learn, take ownership, and grow into production-grade ML work.

• A team player with good communication skills.

Nice to have

- Experience with both CV and NLP / multi-modal models - Exposure to large-scale data processing (Spark, Databricks, or similar) - Contributions to open-source or published research