Artificial Intelligence Research Lead - Vice President

• Work on multiple commercially oriented research projects in collaboration with internal data scientists, applied engineering teams and stakeholders across businesses e.g. Commercial & Investment Banking (including Markets), Asset & Wealth Management, Consumer & Community Banking, etc.

• Formulate problems, generate hypotheses, develop new algorithms and models, conduct experiments and rigorous evaluations, synthesize and communicate results, and deliver well-tested, high-quality code.

• Contribute to high-impact business applications, reusable assets and products, and research initiatives.

• Provide thought leadership on internal and external forums through white papers, publications and presentations.

• Lead projects or major workstreams in larger projects/initiatives. Play a key role in ensuring that problem definitions and solutions are technically sound, generalizable and capture business/product requirements. Proactively identify and propose new research projects linked to business problems.

• PhD in Computer Science, Engineering, or related fields with relevant research experience in AI/ML and 2+ years of relevant work experience

• Research publications in top-tier AI/ML venues (e.g., conferences, journals) – broad conferences such as NeurIPS, ICML, ICLR, etc., or highly regarded specialized conferences such as ICAPS, CRYPTO, etc. 

• Deep understanding of fundamental AI/ML techniques and a strong grasp of current state of the art in specific areas of expertise. Practical experience with statistical data analysis, experimental design and creation of meaningful benchmarks and metrics for evaluation.

• Effective verbal and written communication skills with the ability to address both technical and business audiences.

• Practical software development experience in collaborative project settings such as open-source projects or industry experience. Ability to deliver modular, optimized, high-quality Python code with tests.

• Proven ability to translate business requirements into technical problem formations and deliver novel solutions into production and motivated to make an impact on problems in the financial services domain. 

• Researchers across multiple areas of specialization, including: Synthetic Data Generation across multiple modalities (tabular, images, time-series, etc.), synthetic personas and agent simulations

• Multimodal agent security and safety, alignment, guardrails, red teaming, data privacy and unlearning

• Continual learning for agents (e.g. memory-based, model-based including reinforcement learning and post-training)

• Advanced multi-agent systems including co-ordination, negotiation, deep research, and hybrid / neuro-symbolic approaches

• Formal methods (e.g., temporal logic, constraint programming, model checking, automated planning, theorem proving, SMT/SAT solving) and program/hardware verification.

• Practical experience with ML libraries (e.g. PyTorch, TensorFlow/Keras, HuggingFace Transformers etc.) and agent frameworks (e.g. LangGraph, Google ADK, etc.)

• Familiarity with common formal verification tools and languages (e.g., Coq/Isabelle/Lean, TLA+, Alloy, Z3, PDDL) 

• Curiosity, creativity, resourcefulness, and a collaborative spirit 

• Foundation model training for tabular, time-series, language and other modalities and Multimodal information discovery, understanding and predictive modeling spanning multiple modalities e.g. websites, news, audio, speech, documents, images, etc. 

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