Data Scientist

We are looking for a Data Scientist to build and scale Maya’s intelligence layer. You will be responsible for the end-to-end development of production-ready machine learning solutions designed to address intricate business problems. By aligning deeply with cross-functional requirements, you will ensure our data assets are fully leveraged to achieve aggressive business milestones.

NATURE OF WORK

• Designing production-grade data science solutions to handle marketing and personalization models at scale.
• You are responsible for developing sophisticated machine learning and AI models from inception through to production deployment.
• You serve as the primary architect who dissects complex business challenges into precise requirements for AI solutions.
• Work closely with collaborators to ensure data is leveraged effectively to achieve specific company targets and marketing goals.
• Collaborate with business leaders, engineers, and other stakeholders to formulate and execute a comprehensive AI strategy tailored to the bank's needs.
• Be able to do deep dive research in advancing our pool of technical approaches and advocate for unconventional and novel applications of new algorithms to keep Maya’s marketing engine competitive.
• You validate the technical approaches, methodologies, and scripts of other developers to ensure they align with both technical standards and business requirements.
• You translate complex technical concepts and model results for non-technical audiences and senior stakeholders.
• You maintain an unwavering commitment to quality in all deliverables, ensuring models are robust and "production-grade" and scalable.
• You mentor both senior and junior developers on technical skills and the "soft skills" required to manage stakeholders.

REQUIRED QUALIFICATIONS

• With at least a bachelor’s degree in any quantitative discipline (i.e. Industrial Engineering, Statistics, Physics, Math, Computer Science, etc.)
• Having at least 5 years of relevant experience
• With experience developing models for use of marketing and personalization
• Exposure in recommendation system is a plus
• Exposure in experimentation is a plus
• Exposure in optimization, causal ML and deep learning is a plus