At a Glance
- Tasks: Lead innovative machine learning projects that shape the future of global finance.
- Company: Join J.P. Morgan, a global leader in financial services and innovation.
- Benefits: Competitive salary, diverse team, and opportunities for career growth.
- Other info: Dynamic environment with a focus on diversity, inclusion, and continuous learning.
- Why this job: Make a real impact in payments technology while collaborating with talented professionals.
- Qualifications: Master's degree or equivalent experience in a quantitative field; strong ML skills required.
The predicted salary is between 80000 - 100000 € per year.
Join us at the forefront of payments innovation, where your expertise in machine learning will shape the future of global finance. You will have the opportunity to deliver meaningful impact, collaborate with talented teams, and grow your career in a dynamic environment. We value your unique perspective and commitment to excellence. At J.P. Morgan, you can push the boundaries of what’s possible and help connect businesses and consumers worldwide.
As a Senior Machine Learning Data Scientist on the Payments Machine Learning team, you will lead the end-to-end delivery of advanced machine learning applications. You will work closely with cross-functional partners to drive measurable business outcomes and mentor others in best practices. You will help shape the team’s culture of innovation, collaboration, and continuous learning. Your work will directly influence the evolution of payments technology and its impact on the global economy.
Job Responsibilities
- Lead end-to-end delivery of machine learning and AI solutions for complex Payments and Banking Operations challenges, from discovery to production rollout and lifecycle management.
- Develop innovative ML-based solutions, including GenAI and agentic approaches, and define evaluation, safety, and monitoring strategies for production use.
- Own production deployment patterns, including containerization, CI/CD, automated testing, model registries, governance, monitoring, alerting, and rollback strategies.
- Architect and deploy scalable, reliable, and secure ML services integrated with strategic platforms and downstream consumers (APIs, batch, streaming), meeting SLAs and SLOs.
- Partner with product, operations, risk/control, and technology teams to influence roadmaps, align on requirements, and deliver data-driven transformations.
- Establish reusable, modular data science and machine learning capabilities and patterns scalable across multiple use cases.
- Provide technical leadership and mentorship through code reviews, design reviews, best practices, and upskilling across data science and engineering partners.
- Communicate clearly with technical and non-technical stakeholders, translating model outputs into actionable decisions and operational plans.
- Maintain strong documentation for approaches, model cards, runbooks, and operational procedures.
Required Qualifications, Capabilities, And Skills
- Master’s degree in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, Econometrics) or Bachelor’s degree with equivalent relevant experience.
- Deep understanding of machine learning and AI fundamentals, with strong applied data analysis skills and experience with rigorous evaluation and measurement in real-world settings.
- Proven experience deploying and operating machine learning models in production at scale, including observability, reliability, incident management, and continuous improvement.
- Proficiency in Python software engineering, including production-grade, modular OOP design, testing, performance tuning, and debugging.
- Familiarity with MLOps and distributed systems, including training and serving patterns, batch and real-time architectures, feature stores, orchestration, and scalable data processing.
- Ability to design evaluations aligned with business goals, including offline and online alignment and guardrails for unintended outcomes.
- Experience working in regulated environments with awareness of model risk, controls, privacy, security, and audit-ready documentation.
- Strong problem-solving, communication, stakeholder management, and teamwork skills, with a results-driven mindset and client focus.
Preferred Qualifications, Capabilities, And Skills
- Experience with NLP and/or GenAI (LLMs, retrieval-augmented generation, tool/function calling, agentic workflows), including evaluation and safety patterns.
- Expertise with machine learning frameworks and data science packages (e.g., PyTorch, TensorFlow, Scikit-Learn, NumPy, Pandas, SciPy, statsmodels).
- Experience deploying to AWS (e.g., SageMaker, Bedrock) and operating production workloads with attention to cost, performance, security, and scaling.
- Experience integrating human-in-the-loop or user feedback signals into iterative improvement processes.
If you’re ready to make a lasting impact in a fast-evolving industry and grow your career with a diverse, collaborative team, we invite you to apply and join us on this exciting journey.
About Us
J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company.
About The Team
J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.
Applied AI ML Lead - Payments employer: hackajob
At J.P. Morgan, we pride ourselves on being a premier employer in the financial services sector, offering a dynamic work environment that fosters innovation and collaboration. As an Applied AI ML Lead in Payments, you will not only have the opportunity to influence the future of global finance but also benefit from our commitment to employee growth through mentorship and continuous learning. Our inclusive culture values diverse perspectives, ensuring that every team member can contribute meaningfully while enjoying a range of benefits designed to support both personal and professional development.
StudySmarter Expert Advice🤫
We think this is how you could land Applied AI ML Lead - Payments
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, especially those at J.P. Morgan or similar companies. A friendly chat can sometimes lead to opportunities that aren’t even advertised.
✨Tip Number 2
Prepare for interviews by brushing up on your technical skills and understanding the latest trends in AI and machine learning. We recommend doing mock interviews with friends or using online platforms to get comfortable with the process.
✨Tip Number 3
Showcase your projects! Whether it’s a GitHub repository or a portfolio website, having tangible examples of your work can really set you apart. Make sure to highlight any relevant experience with ML applications in payments.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always looking for passionate individuals who want to make an impact in the finance world.
We think you need these skills to ace Applied AI ML Lead - Payments
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Applied AI ML Lead role. Highlight your experience with machine learning and how it aligns with the job description. We want to see how your unique skills can contribute to our team!
Showcase Your Projects:Include specific examples of your past projects, especially those involving machine learning in payments or banking. This will help us understand your hands-on experience and how you tackle real-world challenges.
Be Clear and Concise:When writing your application, keep it straightforward and to the point. Use clear language to explain your achievements and technical skills. We appreciate clarity as it helps us quickly grasp your qualifications.
Apply Through Our Website:Don’t forget to submit your application through our official website! It’s the best way for us to receive your details and ensures you’re considered for the role. We can’t wait to see what you bring to the table!
How to prepare for a job interview at hackajob
✨Know Your ML Fundamentals
Make sure you have a solid grasp of machine learning and AI fundamentals. Brush up on your knowledge of algorithms, evaluation metrics, and real-world applications. Being able to discuss these concepts confidently will show that you're not just familiar with the theory but can apply it effectively.
✨Showcase Your Deployment Experience
Be prepared to talk about your experience deploying machine learning models in production. Highlight specific projects where you've managed observability, reliability, and incident management. This will demonstrate your ability to handle the complexities of real-world applications, which is crucial for this role.
✨Communicate Clearly with Stakeholders
Practice translating technical jargon into layman's terms. You’ll need to communicate effectively with both technical and non-technical stakeholders. Think of examples where you've successfully conveyed complex ideas and how they influenced decision-making.
✨Prepare for Scenario-Based Questions
Expect scenario-based questions that assess your problem-solving skills and ability to design evaluations aligned with business goals. Prepare by thinking through potential challenges you might face in the role and how you would address them, especially in regulated environments.
