Algorithm Expert, Financial

Job Responsibilities: Financial Behavior Sequence Foundation Model — Pre-training objectives, tokenization architecture, scaling, multi-source data mixing ratios, Account-Card dual-dimension and cross-temporal-scale fusion are all open research questions on this line; Representation Learning and Self-Supervised Learning — Contrastive learning, masked prediction, cross-modal fusion, enabling one set of representations to serve multiple downstream scenarios; Identity Recognition and Biometric Verification — Model capabilities in remote identity verification, liveness detection, comparison, and anti-attack, supporting identity trust across the full chain of account opening and payment; Customer Qualification and Fact Feature Mining — Not recording behavior, but inferring undisclosed objective facts about users from indirect signals — inferring relationship and gang association networks based on graph neural networks, mining macro fact features such as residence and business district based on remote sensing / geographic data; Risk Control and Credit Modeling Deployment — Integrating frontier representations into card fraud detection, credit scoring, and other scenarios, designing blending and fine-tuning, and completing online deployment.

Required: Master's degree or above in Computer Science / Mathematics / Statistics or related fields; 3+ years of deep learning R&D experience, with experience building and completing the full model training pipeline from scratch (including pre-training or large-scale training); Proficient in Transformer and its variants (GPT / BERT / FT-Transformer / MoE), with practical experience in sequence modeling or representation learning; Familiar with mainstream deep learning frameworks (PyTorch preferred), with distributed training (multi-GPU / multi-node) experience; Solid experimental design ability: able to independently conduct ablation studies, scaling law experiments, and reach reliable conclusions.

Preferred: Experience in the financial domain (risk control, anti-fraud, credit scoring, etc.); Foundation Model / self-supervised learning publications in top conferences (NeurIPS / ICML / ICLR / KDD / WWW, etc.); Experience in contrastive learning, ELECTRA, cross-modal fusion, time series / event sequence modeling (Time Mixer / TrajGPT, etc.), graph neural networks / graph anomaly detection.