M1 - Analytics Chapter Lead

Spin is FEMSA’s business unit that enriches and simplifies people's lives. It is an ecosystem of financial and digital solutions that creates added value by helping our users and communities make the most of their time and money. The Spin ecosystem consists of simple, agile, and accessible solutions that help our customers address everyday needs and receive rewards for doing so; such as the digital wallet, Spin by OXXO, the loyalty program, Spin Premia, and Spin Negocios, which offers various solutions for businesses, including NetPay and OXXO PAY.

Objective of the Role

We are seeking an Analytics Chapter Lead focused on enabling high-impact, data-driven practices across the organization. This is a key first-level management role responsible for ensuring technical excellence, experimentation maturity, and scalable solutions in diverse business environments.

Your mission is to empower squads with analysts that are technically solid, business-savvy, and well-supported. You'll work closely with Tribe Leads, Product Managers, and cross-functional stakeholders to ensure that analytics efforts are strategic, consistent, and grounded in best practices across experimentation, cloud architecture, and business impact.

You’ll report directly to the Head of Analytics Chapter, collaborating closely with other Chapter Leads to build a thriving analytics community.

Main Responsibilities

Team Leadership & Management

• Lead and mentor a team of Data Analysts and Data Scientists embedded in squads, promoting technical excellence, continuous learning, and impactful delivery.

Experimentation & Causal Inference

• Establish and enforce standards for statistical analysis, hypothesis validation, A/B testing, quasi-experimental designs, causal inference, and experimentation governance.
• Act as a thought partner to guide, correct, and challenge analysts in their research design and validation approaches.
• Build frameworks for consistent measurement of impact and knowledge transfer across squads.

Cloud-Aware Delivery

• Drive adoption of modern cloud architectures and tools (data lakehouse, orchestration, pipelines, serverless capabilities) to ensure deliveries are efficient, scalable, and cost-optimized.
• Continuously evaluate and challenge existing processes with new innovations in the cloud ecosystem.
• Partner with Data Engineering and MLOps to align analysts with best-fit tools and data flows.

Machine Learning Lifecycle & Architecture

• Oversee the full lifecycle of ML initiatives: ideation, experimentation and model selection, training, validation, deployment, and monitoring.
• Ensure best practices in model governance, reproducibility, and scalability.
• Challenge and support data scientists in designing architectures that balance experimentation with delivery to production.

Business Impact & Standardization

• Guarantee that analytical efforts are tied to measurable business outcomes under common frameworks, metrics, and documentation standards.
• Develop reusable assets, playbooks, and rituals that allow squads to scale learnings and ensure comparability across domains.
• Balance transversal consistency with the flexibility required by each squad’s business reality.

Practice Development

• Co-own the definition and implementation of best practices in experimentation, KPI design, query optimization, documentation, and data storytelling.
• Contribute to building a culture of evidence-based decision making across the company.

Strategic Support & Collaboration

• Serve as an analytical thought partner for Tribe Leads and PMs, ensuring analysts influence decision-making with high-quality insights.
• Actively contribute to Chapter initiatives (standards, rituals, tools, onboarding, community) and foster collaboration with other vertical leads.

Talent Development

• Conduct regular 1:1s, performance reviews, and development planning.
• Identify growth opportunities for analysts in experimentation, cloud, and business translation skills.

Required Knowledge and Experience

• Experience: Minimum 7 years in Analytics, Data Science, preferably in fintech, commercial operations. Experience working with partner-facing or transversal initiatives is a strong plus.
• Leadership: Proven experience leading mixed teams of analysts and/or data scientists in a matrixed or cross-functional environment.
• Experimentation & Causal Methods: Deep expertise in A/B testing, experimentation platforms, quasi-experiments, causal inference methods, hypothesis validation frameworks, and related governance.
• Cloud & Architecture: Strong understanding of data warehousing, data pipelines, and cloud environments (AWS, GCP, or Azure) to ensure deliveries are scalable, cost-efficient, and aligned with modern architectures.
• Technical Skills: Advanced proficiency in SQL and analytical tools such as Python (Pandas, NumPy) or similar. Advanced proficiency in applied statistics for data analysis.
• Visualization: Solid experience with BI tools like Looker, Power BI, or Tableau to build decision-ready dashboards and monitoring solutions.
• Machine Learning Lifecycle: Solid understanding of ML development and architecture (training, validation, selection, deployment, monitoring, and governance) to effectively lead and challenge data scientists.
• Soft Skills: Strong communication, stakeholder management, and collaborative mindset.
• Education: Bachelor’s degree in a quantitative field (Engineering, Economics, Mathematics, etc.). Master’s degree is a plus.

Spin está comprometida con un lugar de trabajo diverso e inclusivo. 
Somos un empleador que ofrece igualdad de oportunidades y no discrimina por motivos de raza, origen nacional, género, identidad de género, orientación sexual, discapacidad, edad u otra condición legalmente protegida. 
Si desea solicitar una adaptación, notifique a su Reclutador.