Senior Data Scientist

Kueski; Región Centro; Presencial; MXN 1.200.000 - 2.100.000; Jornada completa

Descripción de la vacante

Kueski is seeking a Senior Data Scientist in Mexico to develop and refine machine learning models focused on fraud detection and prevention. You will own models end-to-end, from experimentation to deployment, working with cross-functional teams to align ML solutions with product goals.

The role demands a strong quantitative background, Python and SQL proficiency, and experience applying ML in a product context. Fluency in English is essential for communication across teams.

Formación

• Quantitative background in engineering/physics/math.
• Strong Python and ML libraries experience.
• Solid SQL proficiency.
• Fluency in English for cross-functional communication.
• Experience delivering ML projects in product or business context.

Responsabilidades

• Build and improve ML models for fraud detection and product features.
• Monitor production models and diagnose anomalies.
• Collaborate with Product, Engineering, Risk, and Business teams.
• Develop and maintain reliable ML pipelines and experiments.
• Mentor junior team members and contribute to best practices.

Conocimientos

Python & ML libs

SQL

Unix-like

Analytical communication

English fluency

Educación

Quantitative background in Engineering/Physics/Math

Herramientas

AWS ML services

AI-powered tools

Descripción del empleo

About Kueski

At Kueski, we're dedicated to improving the financial lives of people in Mexico. Since 2012, we've been the leading buy now, pay later (BNPL) and online consumer credit platform in Latin America, known for our innovative financial services. Our flagship product, Kueski Pay, provides seamless payment solutions for both online and in-store transactions, establishing itself as the preferred option for nearly 30% of Mexico's top e-commerce merchants. Notably, we were the first to introduce BNPL on Amazon Mexico. We're a tech company with a culture geared toward innovation, collaboration, and impact, fostering a strong, diverse, and inclusive workplace. Our commitment to excellence and ethical business practices has earned us multiple industry recognitions. In 2024, we were named one of the World’s Top FinTech Companies by CNBC and recognized as one of the most ethical companies in Mexico by AMITAI. Additionally, we were certified as a Best Place to Work for LGBTQ+ Equality by HRC Equidad MX 2025 and ranked among the Best Companies for Female Talent by EFY.

Position

Kueski is seeking a Senior Data Scientist to develop and improve machine learning solutions that directly shape our products and customer experiences, with a strong focus on fraud detection and prevention. This role is ideal for an analytical and pragmatic Data Scientist with strong quantitative foundations who is passionate about applying machine learning to product development and solving meaningful business problems, particularly those related to identifying, modeling, and mitigating fraudulent behavior across our lending and payments products. You will combine machine learning, deep-dive analytics, experimentation, and strong business understanding to develop and improve our products. You will own machine learning models throughout their lifecycle, from experimentation and feature engineering to production monitoring and continuous improvement, while contributing to structured and reliable ML pipelines using modern open-source technologies and cloud-based tools. Reporting to the Manager of Data Science, you will work autonomously with cross-functional teams, provide technical input throughout the product development lifecycle, and help raise the technical capabilities and performance of the broader team.

Key Responsibilities

• Build and improve Kueski’s products through machine learning and data analytics, partnering with Product, Engineering, Risk, and Business teams
• Own production ML models end-to-end, monitoring performance, analyzing user and model behavior, and addressing anomalies or underperformance
• Leverage AI-enabled tools and emerging AI capabilities to accelerate analysis, experimentation, and model development, while identifying opportunities to improve products and business outcomes
• Develop model experiments and features, and contribute to reliable, well-documented production ML pipelines
• Provide Data Science expertise throughout the product development lifecycle, including defining requirements for engineering work that impacts ML solutions
• Work autonomously across teams to design and deliver high-impact product enhancements
• Mentor team members, promote technical excellence through pairing and code reviews, and support technical recruiting when needed
• Collaborate with Machine Learning and Software Engineers to strengthen testing and quality processes for production models

Experience

• Quantitative background such as Engineering, Physics, Mathematics, or equivalent practical experience
• Hands‑on experience using AI‑powered tools and emerging AI technologies to improve Data Science workflows, experimentation, analysis, or product solutions
• Strong analytical and communication skills, with the ability to explain complex topics to technical and non-technical audiences
• Advanced understanding of machine learning and hands‑on experience applying ML in academic or industry settings
• Proven ability to solve business problems with cross‑functional teams and influence stakeholders constructively
• Demonstrated autonomy delivering high‑impact Data Science projects, contributing to team performance, and mentoring junior team members
• Strong Python and ML libraries experience, solid SQL proficiency, and comfort working in Unix‑like environments
• Ability to quickly learn and adopt new technologies and methodologies
• Fluency in English and the ability to communicate clearly with non-technical audiences

Nice to have

• Experience detecting organized or coordinated fraud using graph analytics, network science, or device fingerprinting/behavioral biometrics.
• Experience designing or improving hybrid rule‑based + ML systems for real‑time decisioning (e.g. transaction authorization, origination scoring).
• Familiarity with relevant fintech regulatory frameworks (AML, KYC) and their intersection with fraud detection models.
• Background in Risk, Economics, or Pricing, with the ability to apply this knowledge to credit or customer behavior decisions.
• Experience with AWS machine learning services
• Experience applying causal inference techniques, such as uplift modeling or treatment effect estimation, particularly in offering and optimization use cases.
• Background in Economics, Econometrics, Finance, or financial modeling, with the ability to apply this knowledge to credit, pricing, and customer behavior decisions

Diversity & Inclusion

At Kueski we embrace diversity in all forms, systematically promote equity, and ensure everyone feels included with a sense of belonging. We are committed to the full inclusion of all qualified candidates. As part of this commitment, we will make efforts to ensure reasonable accommodations are made during the hiring process. If reasonable accommodation is needed, please let the Talent Acquisition team know.