Applied Scientist Intern - ML / AI

Responsibilities

• As a Data Science (ML and AI) Intern, you won't be working on side projects; you'll be tackling real problems, influencing product direction, and working alongside a team that believes in asking why, not just how.
• Assist in developing and improving credit and fraud risk models using machine learning, deep learning, and rules-based approaches under the guidance of senior data scientists.
• Support the analysis of alternative data sources (e. g., mobile device data, bank statements) to enhance risk assessment and decision-making models.
• Help build and evaluate models related to customer behavior, including personalization, recommendations, referrals, and transaction categorization.
• Learn and contribute to the end-to-end machine learning pipeline from data collection and preprocessing to model development and basic production deployment.
• Participate in monitoring and evaluating the performance of models in production, learning how to identify issues and suggest improvements.
• Assist in data gathering, cleaning, and exploratory data analysis to support various data science projects and business initiatives.
• Engage with business and technical stakeholders to understand their data needs and help identify potential data-driven solutions.
  

Notable Achievements

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

Requirements

• Proficient in Python programming, with a focus on coding for machine learning and deep learning applications.
• Strong in data analysis and data wrangling.
• Follow industry best practices and stay up to date with the latest machine learning/ GenAI algorithms and techniques to drive innovation.
• Knowledge of deep learning concepts like CNN, RNN, tokenization, transformers, and various NLP techniques.
• Bachelor's or Master's degree in Computer Science, Information Management, Statistics, or related field.
  

Soft Skills

• Highly curious and passionate about problem-solving, with a strong drive to uncover insights and optimize solutions.
• Exceptionally detail-oriented, capable of working both independently and collaboratively in a fast-paced environment.
• Excellent communication skills, with the ability to translate complex concepts into clear, actionable insights for both technical and non-technical stakeholders.
• Energetic and proactive, with a strong learning aptitude and the audacity to take on challenges beyond their comfort zone.
• Not afraid to go above and beyond, demonstrating initiative and ownership in tackling tough problems.
  

Nice To Have

• Publications or presentations in recognized Machine Learning and Data Science journals/conferences.
• Experience with cloud services (like AWS or Google Cloud) and understanding of distributed systems.
• Exposure to GenAI models.
• Languages: Spanish, English (Level: advanced).