Executive Director - Data Scientist

JPMC is hiring the best talents to join the growing Asset and Wealth Management team. We are executing like a startup and building next-generation technology that combines JPMC unique data and full-service advantage to develop high impact AI applications and platforms in the financial services industry. We are looking for hands-on ML Engineering leader and expert who is excited about the opportunity. As an Applied Data Science Director Director of Data Science and AI , you will will lead the strategy, delivery, and adoption of applied data science solutions that drive measurable business outcomes. This leader will partner with product, engineering, and business stakeholders to identify high-value opportunities, build scalable ML/AI solutions, and operationalize models in production using modern MLOps practices. The role combines hands-on technical depth with people leadership, governance, and executive communication. You'll combine your years of proven development expertise with a never-ending quest to create innovative technology through solid engineering practices. Your passion and experience in one or more technology domains will help solve complex business problems to serve our Private Bank clients. As a constant learner and early adopter, you're already embracing leading-edge technologies and methodologies; your example encourages others to follow suit. Job Responsibilities Define applied DS strategy and roadmap aligned to business priorities; identify, size, and prioritize use cases based on value, feasibility, and risk.

Lead end-to-end ML lifecycle:

problem framing, data assessment, feature engineering, model development, validation, deployment, monitoring, and iteration. Deliver production-grade solutions in close partnership with Engineering and Product, ensuring scalable architectures, reliability, and maintainability.

Drive experimentation and measurement:

establish A/B testing and causal inference approaches, define success metrics, and quantify business impact.

Implement MLOps standards:

CI/CD for ML, model registries, automated retraining, drift monitoring, and reproducible pipelines.

Ensure responsible AI and governance:

model risk management, explainability, bias/fairness, privacy, documentation, and audit readiness. Lead and grow a high-performing team: hiring, coaching, performance management, career development, and best-practice communities.

Influence stakeholders and executive audiences:

communicate tradeoffs, recommendations, and results clearly; drive alignment across functions.

Establish DS operating mechanisms:

intake processes, review boards (model/design), documentation standards, and delivery playbooks. Promote data-driven culture by enabling self-service analytics, raising data literacy, and strengthening decision frameworks. Required qualifications, capabilities and skills Bachelors or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or related field and m inimum 10 years of experience in data science/ML/analytics with 5+ years in leadership/management roles. Proven record delivering applied ML/AI solutions with measurable impact in production environments and strong foundation in statistics and ML (supervised/unsupervised learning, model evaluation, uncertainty, experimentation). Excellent stakeholder management skills; ability to translate business problems into DS solutions and influence decision and collaborate with firmwide ML teams, Business and Product Partners, peers in geographically dispersed teams, and colleagues across JPMorgan AWM's lines of business and functions to drive alignment, accelerate adoption of common AI capabilities, and deliver impactful solutions Ability to drive standards: documentation, code quality, reproducibility, review processes. Experience with GenAI/LLMs (prompting, RAG, evaluation, guardrails), or AI product delivery. Must have strong programming skills in Python, Go, or Java .

ML/AI:

classification/regression, tree-based models, NLP, time series, recommendation, anomaly detection (as applicable).

Experimentation:

A/B testing, causal inference, uplift modeling, metric design, statistical power. Ability to partner effectively with Data Engineers for data modeling, pipelines (batch/stream), feature stores. Expertise in

MLOps & Production:

model deployment patterns (batch/real-time), monitoring, drift detection, retraining, model registry, CI/CD for ML. Preferred qualifications, capabilities and skills

ML/AI:

classification/regression, tree-based models, NLP, time series, recommendation, anomaly detection (as applicable).

Experimentation:

A/B testing, causal inference, uplift modeling, metric design, statistical power. Ability to partner effectively with Data Engineers for data modeling, pipelines (batch/stream), feature stores. Expertise in

MLOps & Production:

model deployment patterns (batch/real-time), monitoring, drift detection, retraining, model registry, CI/CD for ML.

Cloud & Platforms:

AWS and/or Azure (compute, storage, orchestration); containerization (Docker) and Kubernetes familiarity. Strong written and verbal communication; executive-ready narratives and crisp decision materials.