Senior Data Scientist — Computer Vision & NLP

Who We Are

DV is the leader in digital performance solutions, helping our advertiser and agency partners Verify the quality of their digital campaigns, Optimise to improve performance and Prove that they're achieving their business outcomes, through unbiased 3rd party data and analytics. DV's mission is to be the definitive source of transparency and data-driven insights into the quality and effectiveness of digital advertising for the world's largest brands, agencies, publishers, and digital ad platforms. Since 2008, DV has helped hundreds of Fortune 500 companies gain the most from their media spend by delivering best-in-class solutions across the digital advertising ecosystem, helping to build a better industry. Learn more at www.doubleverify.com.

What You’ll Do

You will be a senior technical owner within the Data Science team, driving key classification workstreams end-to-end with a high degree of autonomy.

This is a fully hands-on role. You'll own complex modeling problems from problem definition to production, set technical direction for your area, and raise the bar for the people around you.

You will work on core systems that operate at massive scale, where:

• data is abundant but labels are scarce and expensive
• problems are long-tail and ambiguous
• systems must meet strict latency and cost constraints (pre-bid)

Key Responsibilities

• Own and drive content classification systems across social platforms (Meta, TikTok, YouTube), web, apps, and emerging surfaces such as conversational AI
• Design, train, and fine-tune models across NLP, computer vision, and multimodal pipelines, including adapting foundation and embedding models to DV's policy definitions

Own the full lifecycle

data selection -> labeling strategy -> training -> evaluation -> deployment

• Develop strategies for efficient data curation and labeling (active learning, auto-labeling, LLM-based data generation, sampling under scale)
• Improve model quality (precision/recall) while balancing cost, latency, and scale

Build rigorous evaluation methodology

test set design, data leakage prevention, and statistically sound comparisons

• Apply modern AI approaches (LLMs, embeddings, foundation models, agentic workflows) to real production problems

Act as a technical leader for the team

mentor other Data Scientists, review approaches, and build reusable tools and playbooks that scale the team's output

• Work closely with ML Engineering, Product, and Policy to translate ambiguous requirements into scalable systems

Who You Are

• 6+ years of hands-on experience in Machine Learning / Deep Learning
• MSc or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field
• Strong background in NLP and/or Computer Vision
• Track record of owning ML systems end-to-end, from problem framing and data strategy through deployment and monitoring, at scale
• Strong understanding of real-world trade-offs (accuracy, cost, latency)
• Able to own ambiguous problems independently, from framing through delivery

What We’re Looking For

• Hands-on experience with deep learning frameworks (PyTorch / TensorFlow)
• Hands-on experience fine-tuning and deploying deep learning models (e.g., transformers) to production
• Experience working with large datasets and model evaluation pipelines

Advantages

• Experience fine-tuning embedding models or foundation models for domain-specific tasks
• Experience leveraging LLMs and other foundation models to extend team capabilities (e.g., labeling, data generation, evaluation)
• Experience with multimodal systems (vision + text + audio)
• Experience with multilingual NLP
• Experience with active learning or data-centric AI
• Experience mentoring other data scientists or leading technical projects

Why This Role

You will work on one of DV's most critical problems: building best-in-class classification systems under real-world constraints of scale, cost, and latency. This role offers deep technical ownership, high business impact, and a clear path to technical or people leadership.