Data Scientist

• Design and develop novel deep learning architectures (e.g., Transformer, Graph Neural Network-based) for large-scale, federated pre-training on unlabeled or partially labeled data distributed across multiple sources.
• Implement and advance state-of-the-art semi-supervised and self-supervised learning algorithms (e.g., contrastive learning, masked auto-encoding) tailored for the unique constraints of federated learning, such as communication bottlenecks and data heterogeneity.
• Develop and implement robust and communication-efficient federated aggregation strategies (e.g., FedAvg, FedProx, SCAFFOLD) that are stable for large, complex models and can handle non-IID (non-independently and identically distributed) data.
• Create efficient and effective protocols for fine-tuning and adapting the pre-trained federated foundation models for a wide range of specific downstream tasks, ensuring knowledge transfer while maintaining privacy.
• Collaborate with data engineering teams to establish pipelines for accessing and simulating distributed datasets.
• Develop high-fidelity simulation environments to test, debug, and benchmark federated pre-training strategies before real-world deployment.
• Profile, analyze, and optimize the computational performance (e.g., memory, latency, communication cost) of federated training and inference to ensure scalability to a large number of clients and massive datasets.
• Author high-impact research papers for publication in top-tier machine learning conferences (e.g., NeurIPS, ICML, ICLR) and relevant scientific journals. Prepare and deliver compelling presentations to both internal and external audiences.
• Write clean, high-quality, and reproducible code. Contribute to internal libraries and ML platforms. Implement version control for data, code, and models to ensure robust and transparent research.
• Work in a collaborative, multi-disciplinary team alongside software engineers, MLOps specialists, privacy experts, and domain scientists to translate research concepts into practical, impactful solutions.
• Maintain a thorough understanding of the latest advancements in federated learning, deep learning, and related fields to drive innovation and contribute to the team's research strategy.

Advisor, Federated Learning Data Scientist

Data Science & Analytics Director

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