About Aplazo

Aplazo, a Mexican BNPL startup, offers a unique solution for an underbanked Population. Unlike its international counterparts, Aplazo isn't just about debt - it's an alternative to cash for many Mexicans. Founded four years ago, Aplazo allows users to make fractionated payments online and in stores, even without a credit card. We are on a mission to unlock financial access, freedom, and opportunity for every Mexican. Now our users can have what they want and when they want. Aplazo allows its consumers to live their ideal lifestyle, making it easier to acquire the products they want. Through our platform, consumers can split their online and offline purchases into multiple installments without needing a credit card and avoiding the debt trap.

In-store transactions account for more than half of Aplazo's business, highlighting the limited reach of e-commerce in Mexico. Aplazo's success is attributed to its focus on data and technology. By leveraging AI, the company has been able to limit credit loss despite its rapid growth. Notably, 40% of Aplazo's users have no credit history, making traditional credit checks irrelevant. This is a major hurdle for international BNPL players who may struggle in the Mexican market.

Aplazo has recently received Series B funding of $70 Million USD and Series A+ funding of $27.5 Million USD in 2022 With a total of $110 million raised Aplazo is well-positioned for continued growth and innovation in the Mexican fintech landscape and its team is growing in each vertical. We are setting out to make an impact on an epic scale. Aplazo enables merchants to connect with consumers in a creative, purposeful way, building more loyalty and trust between them. Aplazo's integrated, tech-enabled platform offers merchants the ability to increase average basket size, conversion, and customer engagement.

Aplazo Story in Techcrunch : https://techcrunch.com/2024/05/13/aplazo/

About ML & AI Labs

At Aplazo, our ML & AI team is a cornerstone of our technological innovation and strategic decision-making. We pride ourselves on our technical maturity and high proficiency in addressing complex and critical business challenges. Our team is dedicated to solving high-stakes problems across various domains, including risk and payments, merchants, B2C, personalisation, growth and marketing, recommendation systems, customer support, collections, underwriting, credit life cycle management, and fraud detection.

Aplazo ML & AI lab has been instrumental in the development of state-of-the-art credit risk and fraud detection models. The risk and payment machine learning models suite has leveraged advanced data science and AI algorithms to achieve best-in-class prediction capabilities. Aplazo has adopted best practices in MLOps, creating a seamless workflow for model development, deployment, and monitoring. This includes automated CI/CD pipelines, version control for models and data, and robust monitoring and logging systems. We have built a sophisticated ML engineering infrastructure that supports scalable model training, testing, and deployment. This infrastructure ensures the high availability, reliability, and performance of our machine learning solutions.

Our expertise in developing advanced recommendation models has significantly enhanced user engagement, driving more personalised and meaningful interactions with our platform. We have also created sophisticated propensity and activation probability models, enabling us to optimise Customer Acquisition Cost (CAC) effectively. These models help us target the right customers with the right offers at the right time, maximising our marketing efficiency.

Moreover, we have engineered cutting-edge data science tools to deepen our understanding of the relationships within our consumer base. These insights have been instrumental in refining our referral programs, leading to increased customer retention and satisfaction. Our commitment to leveraging data science for continuous improvement and innovation ensures that Aplazo remains at the forefront of delivering exceptional value to our customers and stakeholders.

Notable Achievements :

1. We achieved the lowest fraud rate in LATEM markets by leveraging the very high capabilities of our multiple fraud detection models with real-time inferencing .
2. Alternative data lending - built capabilities to extend credits to the underbanked segment in Mexico markets fostering financial inclusion and financial freedom for everyone .
3. Advanced reinforcement learning models and deep learning models to achieve hyper-personalisation for credit limit changes .
4. Incorporated Dynamic Risk Based Pricing model to optimise customer conversion rate .
5. Developed recommendation engine to personalise all communication to end user.

Role Mission :

We are seeking a highly skilled and experienced AI leader to lead our risk and payment initiatives. The ideal candidate will possess extensive expertise in machine learning, deep learning, and natural language processing (NLP), along with a proven track record in managing and mentoring teams. This role requires a strong technical background, excellent leadership abilities, and the capability to collaborate effectively with cross-functional teams and senior leadership.

Key Responsibilities:

• Conduct research and drive innovations to develop next-generation solutions in core functional areas including machine learning, deep learning, NLP, pricing, user personalisation, merchant understanding, and data insights.
• Take ownership of all Risk and payment initiatives from the ML & AI team. Mentor a team of highly talented data scientists and analysts to ensure the delivery of quality projects.
• Provide technical thought leadership, coaching, and mentorship in data science while working closely with engineering and other cross-functional teams.
• Apply data science and deep learning concepts to address core business challenges in credit risk, fraud risk, payments, and collections.
• Collaborate with senior leadership to identify company priorities, set goals, and establish objectives and key results (OKRs) for the team. Assume responsibility for delinquency and profitability, OKRs.
• Evaluate external data for small businesses, including signals and inflection points, and establish proof of concept for investing in data to enhance commercial targeting.
• Develop and maintain cross-functional relationships with stakeholders across the globe (USA, Chile, Europe, and primarily Mexico) and external vendors to ensure effective project prioritisation and execution.
• Identify emerging fraud trends and recommend appropriate mitigation strategies, clearly communicating change recommendations based on data-driven analysis.

Required Qualifications:

Experience:

• Extensive experience in risk management, particularly within alternative lending, buy-now-pay-later, payment, or credit card firms.
• Proficiency in statistical modeling, machine learning, data mining, unstructured data analytics, and natural language processing. Strong understanding of Bayesian Modeling, Classification Models, Cluster Analysis, Neural Networks, Nonparametric Methods, Multivariate Statistics, etc.
• Experience with common libraries and frameworks in data science.
• Over 8 years of hands-on experience in one or more of the following: Artificial Intelligence (AI), Machine Learning (ML), NLP, Deep Learning, Image Processing, Computer Vision, and Neural Networks.
• Experience in mentoring and managing ML projects.
• Excellent communication skills and ability to collaborate with diverse teams.
• Experience with pre-trained language models (e.g., BERT, GPT-3, GPT-4, T5).
• Proficiency in production-level deployment (ML engineering, OOPs) and collaboration with Platforms and Engineering teams to ensure the integration of ML models into systems.

Years of Experience:

• 10 to 15 years of relevant work experience.

Technical Skills:

• Proficiency in Python programming is essential. Strong coding skills in ML and Deep Learning, including regression, classification, and cluster analysis.
• Familiarity with database queries and data analysis processes (SQL, Python).
• Experience with Decision Trees, Random Forests, Gradient Boosting, and XGBoost.
• Commitment to industry best practices and staying updated with the latest ML/GenAI algorithms and techniques.
• Knowledge of deep learning concepts such as CNN, RNN, tokenization, transformers, and various NLP techniques.

Soft Skills:

• Exceptional leadership, influencing, communication, interpersonal, and teamwork skills.