Dynamic Yield, a Mastercard company, is dedicated to connecting and powering an inclusive, digital economy that benefits everyone. Our Data Science & AI team is seeking a Lead ML Engineer to develop cutting-edge analytical products and solutions from vast datasets. The challenge involves creating high-performance algorithms, built on billions of transactions, to derive big data insights that drive business, all while maintaining a keen eye for data privacy and governance.
You will play a pivotal role in leading talent, building robust ML solutions using Mastercard's global data assets, and consulting with clients to translate their needs into impactful data analyses. This is an opportunity to contribute to a culture of inclusion and innovation, making better decisions and delivering superior business results.
The Role
As a Lead ML Engineer, you will:
- Talent Leadership: Lead talent acquisition efforts, facilitate training programs, and conduct performance management for your direct reports.
- ML Solution Development: Lead teams in creating robust ML solutions by effectively utilizing Mastercard's global data assets and software platform.
- Production & Maintenance: Build, productionize, and maintain data-driven AI/ML applications and data processing workflows or pipelines.
- Client & Stakeholder Consultation: Consult with clients and stakeholders to understand and translate their needs into data analyses and/or solutions, ensuring requirements are accurately captured and technically feasible.
- Technical Guidance: Guide others in comprehensive technical analyses and allocate work across teams to ensure the delivery of high-quality, effective solutions.
- Internal Liaison: Liaise with internal stakeholders (e.g., MA TECH, Data Strategy Management, AI governance) to identify and elaborate on opportunities related to analytical solution development, feasibility, and other technical offerings.
- Documentation & Standardization: Lead the development of presentations and technical documentation. Identify and recommend opportunities to standardize and automate efforts to ensure quality and enable scaling of ML products.
- Project Delivery: Meet project deadlines for accountable deliverables, anticipate delays or foreseeable barriers, and escalate issues when necessary.
- Quality Assurance: Conduct due diligence quality assurance testing for prototypes and tools in stage, and resolve recurring complex issues and bugs.
- Process Documentation: Ensure all machine learning processes, from data preparation to model deployment, are well-documented for internal use and compliance.
- Mentorship: Mentor and guide junior developers, fostering their growth and expertise.
All About You
- Big Data Expertise: Proficiency in big data frameworks and tools such as Hadoop, Spark, and Hive.
- Technical Proficiency: Strong programming skills in languages such as Python and SQL.
- Experience with data visualization tools (e.g., Tableau, Power BI) and understanding of cloud computing services (AWS, Azure, GCP) related to data processing and storage is a plus.
- Experience with testing frameworks and test-driven development (TDD) practices.
- Advanced Analytical Skills: Strong applied knowledge and hands-on experience in machine learning algorithms and deep learning frameworks.
- Familiarity with AI and machine learning platforms such as TensorFlow, PyTorch, or similar.
- Familiar with training and deploying models with large datasets, including strategies for parallelizing and optimizing workflows.
- Experience in productionizing of GenAI products is a plus.
- Leadership & Strategic Planning: Proven experience in leading engineering teams, defining vision and strategy for data-driven initiatives, and driving projects from conception to implementation. Ability to mentor and develop talent.
- Problem-Solving Skills: Strong analytical and critical thinking abilities to solve complex problems, along with the creativity to find innovative solutions.
- Communication & Collaboration: Excellent verbal and written communication skills, with the ability to explain complex analytical concepts to non-technical stakeholders. Experience working cross-functionally with departments and flexibility to work as part of diverse, geographically distributed project teams.
- Project Management Skills: Proficiency in managing multiple projects simultaneously, with a focus on delivering results within tight deadlines.
- Responsible AI Knowledge: Awareness of the principles and practices surrounding responsible AI, including fairness, transparency, accountability, and ethics in AI deployments.
- Innovation & Continuous Learning: A mindset geared towards innovation, staying abreast of industry trends and emerging technologies in big data and analytics, and continuously seeking opportunities for personal and professional growth.
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks come with an inherent risk to the organization. Therefore, every person working for, or on behalf of, Mastercard is responsible for information security and must:
- Abide by Mastercard's security policies and practices.
- Ensure the confidentiality and integrity of the information being accessed.
- Report any suspected information security violation or breach.
- Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.
