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DoubleVerify is seeking a Manager of Data Science & Research to lead a team of experienced data scientists while remaining deeply involved in technical work. This is a hands-on leadership role (~70% hands-on) combining direct modeling work with ownership of team direction and execution.
You will work on core systems operating at massive scale, where data is abundant but labels are scarce and expensive, problems are long-tail and ambiguous, and systems must meet strict latency and cost constraints for pre-bid operations.
Key responsibilities include:
- Lead development of content classification systems across social platforms (Meta, TikTok, YouTube), web, and apps
- Design and build models across computer vision, NLP, and multimodal pipelines
- Own the full lifecycle: data selection, labeling strategy, training, evaluation, and deployment
- Develop strategies for efficient data curation and labeling (active learning, auto-labeling, sampling under scale)
- Improve model quality (precision/recall) while balancing cost, latency, and scale
- Drive automation systems (auto-labeling, auto-curation, retraining loops)
- Apply modern AI approaches (LLMs, embeddings, foundation models) to real production problems
- Lead and mentor a team of senior data scientists, setting technical direction and pushing execution forward
- Work closely with ML Engineering, Product, and Policy to translate ambiguous requirements into scalable systems
Required qualifications:
- 3+ years of experience leading data science/ML teams
- 6+ years of hands-on experience in machine learning/deep learning
- Strong background in computer vision and/or NLP
- Experience building and deploying production ML systems at scale
- Strong understanding of real-world trade-offs (accuracy, cost, latency)
- Hands-on experience with deep learning frameworks (PyTorch/TensorFlow)
- Experience with ML/DS tools (scikit-learn, OpenCV, HuggingFace, etc.)
- Experience working with large datasets and model evaluation pipelines
Advantages include experience with multimodal systems, LLMs/embeddings/foundation models, and AutoML/active learning/data-centric AI approaches.
