Manager, Data Science & Research
Indexed description
Manager, Data Science & Research
- Tel Aviv-Israel
- Technology
What you will do
You will lead a team of experienced Data Scientists while remaining deeply involved in the 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 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)
- 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 -> 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
- 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
- Experience with multimodal systems (vision + text + audio)
- Experience with LLMs / embeddings / foundation models
- Experience with AutoML, active learning, or data-centric AI
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