Who we are
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 a massive scale, where:
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Data is abundant, but labels are scarce and expensive
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problems are long-tail and ambiguous
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Systems must meet strict latency and cost constraints (pre-bid)
Your responsibilities include:
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Lead development of content classification systems across social platforms (Meta, TikTok, YouTube), web, and apps
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Design and build models across computer vision, NLP, and multimodal pipelines
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Own the full lifecycle: data selection -> labeling strategy -> training -> evaluation -> deployment
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Develop strategies for efficient data curation and labeling (active learning, auto-labeling, sampling under scale)
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Improve model quality (precision/recall) while balancing cost, latency, and scale
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Drive automation systems (auto-labeling, auto-curation, retraining loops)
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Apply modern AI approaches (LLMs, embeddings, foundation models) to real production problems
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Lead and mentor a team of senior Data Scientists, setting technical direction and pushing execution forward
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Work closely with ML Engineering, Product, and Policy to translate ambiguous requirements into scalable systems
Who you are
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3+ years of experience leading Data Science / ML teams
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6+ years of hands-on experience in Machine Learning / Deep Learning
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Strong background in Computer Vision and/or NLP
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Experience building and deploying production ML systems at scale
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Strong understanding of real-world trade-offs (accuracy, cost, latency)
Technical requirements
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Hands-on experience with deep learning frameworks (PyTorch / TensorFlow)
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Experience with ML/DS tools (scikit-learn, OpenCV, HuggingFace, etc.)
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Experience working with large datasets and model evaluation pipelines
Advantages
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Experience with multimodal systems (vision + text + audio)
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Experience with LLMs / embeddings / foundation models
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Experience with AutoML, active learning, or data-centric AI
#Hybrid#