Applied AIML -Executive Director

Job Responsibilities

• Own and evolve CCB recommendation & personalization platforms (candidate generation, ranking, re-ranking, retrieval, and real-time decisioning) to improve customer relevance and engagement across journeys and surfaces.
• Lead end-to-end ML delivery: problem framing, feature strategy, model development, offline/online evaluation, A/B testing, launch, monitoring, and iteration for production recommender systems.
• Develop and operationalize evaluation frameworks for ranking and personalization (e.g., relevance/utility metrics, calibration, novelty/diversity, long-term value, bias/fairness considerations, and guardrails).
• Apply NLP and search/retrieval techniques to enrich signals (query/document understanding, embeddings, semantic retrieval, entity/intent extraction) that improve recommendation quality and explainability.
• Use GenAI pragmatically to augment the recommendation stack (e.g., content understanding, synthetic labeling, summarization, conversational retrieval, or post-processing) with strong controls, evaluation, and risk awareness.
• Build and lead a high-performing team of applied scientists and ML engineers; set technical direction, raise engineering quality, and provide coaching and career development.
• Partner cross-functionally with Product, Design, Data, Risk/Controls, and Engineering to align on goals, prioritize roadmaps, and deliver measurable customer and business impact.
• Be a hands-on technical leader: contribute to architecture and critical code paths; guide system design for low-latency services, feature pipelines, training/inference infrastructure, and reliability.
• Promote a culture of rigor and learning by introducing modern recommendation methods, experimentation best practices, and strong documentation and knowledge sharing.

Required Qualifications, Capabilities, And Skills

• PhD in Computer Science (or equivalent experience) with strong research and industry background in machine learning, with depth in recommender systems, ranking, personalization, or information retrieval.
• Proven ability to lead and deliver large-scale production ML systems using big data, including recommenders (collaborative filtering, deep retrieval/ranking, sequence models), classification/regression, and causal/experimental methods.
• Strong track record of people leadership (building teams, setting technical direction, mentorship, performance management).
• Excellent written and verbal communication skills, including influencing senior stakeholders and translating business goals into measurable ML outcomes.
• 10+ years of hands-on programming and system-building experience (PhD + industry); strong in Python and at least one of Scala/Java; experience with Spark and distributed data processing.
• Solid fundamentals in data structures, algorithms, distributed systems, and databases, and experience building scalable, reliable ML services.

Preferred Qualifications, Capabilities, And Skills

• Deep expertise in ranking/retrieval/search (semantic retrieval, ANN/vector search, learning-to-rank), online experimentation, and real-time personalization.
• Experience designing feature stores, streaming/real-time pipelines, low-latency inference, and ML observability (data/model drift, performance diagnostics).
• Experience applying NLP/LLMs to improve recommendation systems (embeddings, query understanding, content signals, retrieval augmentation) with disciplined evaluation and governance.
• Familiarity with responsible AI considerations relevant to personalization (fairness, explainability, privacy, and control frameworks).