Lead Data Scientist

Maya; Mandaluyong; On-site; PHP 1,800,000 - 2,600,000; Full time

Job summary

Maya is seeking a Data Science Lead to drive AI, ML, and customer intelligence strategies. You will lead a team to deploy analytics, ML, and AI solutions that boost customer acquisition, engagement, retention, personalization, and revenue across Maya's ecosystem.

The ideal candidate has strong background in clustering, segmentation, propensity modeling, recommendations, experimentation, and marketing analytics, and can translate business goals into scalable AI-powered systems.

Qualifications

• Bachelor's degree in a quantitative field.
• 7+ years in Data Science/AI/Machine Learning.
• 3+ years of people leadership experience.
• Proven experience with clustering, propensity, recommendation, and predictive models.
• Experience supporting marketing, growth, CRM, lifecycle, or customer analytics.
• Partnered with Product, Marketing, Growth, and Engineering stakeholders.

Responsibilities

• Identify opportunities where AI and analytics can drive impact.
• Translate challenges into scalable DS solutions and roadmaps.
• Define project scopes, delivery plans, success metrics, and timelines.
• Measure business impact of AI initiatives and communicate results.
• Promote adoption of AI and experimentation across the organization.
• Lead development and deployment of segmentation, clustering, propensity, and recommendation models.
• Collaborate with Growth and CRM teams to leverage insights in Marketing Platforms and CDPs.
• Mentor and coach DS team members.
• Drive workforce planning and capability building.

Skills

Leadership

Data science

Machine learning

Customer segmentation

Clustering

Propensity modeling

Recommendation systems

Experimentation

Education

Bachelor's degree in Statistics/CS/Math/Data Science

Tools

Marketing Platforms

CDPs

Campaign Management Systems

Job description

We are looking for a Data Science Lead to drive Maya's AI, machine learning, and customer intelligence strategy. This role will lead a team of Data Scientists in developing and deploying advanced analytics, machine learning, and AI solutions that improve customer acquisition, engagement, retention, personalization, and revenue growth across Maya's ecosystem.

The ideal candidate has strong expertise in clustering, customer segmentation, propensity modeling, recommendation systems, experimentation, and marketing analytics, and can translate business objectives into scalable AI-powered solutions. This leader will work closely with Marketing, Product, Growth, Engineering, and Business stakeholders to build and operationalize Maya's intelligence layer while establishing best practices across the Data Science function.

NATURE OF WORLK

Leadership & Delivery

• Identify and prioritize business opportunities where AI, machine learning, and advanced analytics can drive significant impact.
• Translate business challenges into scalable Data Science solutions and technical roadmaps.
• Define project scopes, delivery plans, success metrics, timelines, and resource requirements.
• Measure and communicate the business impact of AI and machine learning initiatives.
• Advocate for the adoption of AI, machine learning, experimentation, and advanced analytics throughout the organization.
• Drive innovation by promoting the use of emerging technologies, methodologies, and algorithms.
• Collaborate with stakeholders to define and execute a comprehensive AI and customer intelligence strategy across the organization.
• Partner with Engineering and Machine Learning Engineering teams to operationalize and scale Data Science solutions.
• Lead the development and deployment of customer segmentation, clustering, propensity, recommendation, and predictive models that support marketing, growth, lifecycle, and retention initiatives.
• Design advanced analytical frameworks that improve customer targeting, personalization, campaign effectiveness, and customer lifetime value.
• Develop models for use cases such as:
• Propensity to Purchase
• Propensity to Churn
• Propensity to Upgrade
• Cross-sell and Upsell Recommendations
• Next Best Action
• Customer Lifetime Value Prediction
• Customer Segmentation and Clustering
• Partner with Growth and CRM teams to leverage machine learning insights within Marketing Platforms, Customer Data Platforms (CDPs), and campaign orchestration tools.
• Drive the adoption of data-driven experimentation, uplift modeling, and causal inference methodologies.

Technical Leadership

• Lead the translation of complex business problems into actionable Data Science frameworks and machine learning solutions.
• Oversee the design, development, validation, and deployment of machine learning and AI models.
• Review methodologies, technical approaches, experimentation frameworks, and code to ensure quality and alignment with business goals.
• Establish standards for model explainability, monitoring, governance, and scalability.
• Present model performance, business impact, and recommendations to executive stakeholders.

People Management

• Mentor and coach both senior and junior Data Scientists.
• Create development plans that align individual career goals with organizational priorities.
• Lead workforce planning, capability building, and team engagement initiatives.
• Drive organizational improvements that strengthen team effectiveness and business outcomes.

REQUIRED QUALIFICATIONS

• Bachelor's degree in Statistics, Computer Science, Mathematics, Data Science, Engineering, or a related quantitative discipline.
• At least 7+ years of experience in Data Science, Machine Learning, or AI, including the development and deployment of production-grade solutions.
• At least 3+ years of people leadership experience managing and developing high-performing Data Science teams.
• Proven experience building and deploying clustering, propensity, recommendation, classification, and predictive models.
• Experience supporting marketing, growth, CRM, lifecycle, personalization, or customer analytics initiatives.
• Experience partnering with Product, Marketing, Growth, and Engineering stakeholders.
• Experience with Marketing Platforms, Customer Data Platforms (CDPs), Campaign Management Systems, or customer engagement platforms is highly preferred.
• Experience within fintech, banking, digital financial services, or large-scale consumer technology companies is highly preferred.
• Product scaling, experimentation, and growth analytics experience is a plus.
• Research and development experience in emerging AI technologies is a plus.