Data Scientist

Klar

10d•Remote

About The Position

Klar is a Mexican fintech startup aiming to democratize financial services in Mexico, where a significant portion of the population is unbanked. Since its 2019 launch, Klar has served over 7 million users with a range of debit and credit products. The company has a strong future roadmap and has partnered with Uber for their first global credit card in Mexico. Klar was recognized as a "Great Place to Work" in Mexico in 2023. The company has a diverse and talented team with a head office in Mexico City and remote tech hubs in Berlin and Argentina, fostering an environment of cultural and linguistic learning. Klar's values include Ownership, Excellence, Inclusion, Customer Obsession, and Klarity (clear and authentic communication).

Requirements

Master's degree or above in Data Science, Computer Science, Statistics, Mathematics, or a related field
5+ years of experience in Data Science, Machine Learning, or a related field
Strong proficiency in Python and SQL
Experience with Machine Learning libraries such as Scikit-Learn, XGBoost, TensorFlow, Keras, and Pytorch
Experience owning models or analytical systems end to end, including performance monitoring, retraining, documentation, and stakeholder alignment
Comfort using AI coding assistants, such as Claude Code and Codex, to improve productivity
Proficiency in data analysis, visualization, and manipulation tools such as Pandas, Matplotlib, and Seaborn
Strong problem-solving skills and the ability to think creatively
Ability to communicate model tradeoffs, risk implications, and business impact to technical and non-technical stakeholders

Nice To Haves

Experience with credit risk, fraud, underwriting, lending, financial services, or another regulated decisioning domain
Experience with dbt (Data Build Tool) to transform SQL data
Experience working with novel, transformer-based architectures

Responsibilities

Design, build, and optimize Machine Learning models powering origination, behavioral scoring, limit management, and early-warning systems
Leverage alternative data sources including transaction behavior, merchant data, and device intelligence to improve predictive performance
Evaluate and implement the infrastructure required to deploy, monitor, and retrain models at scale
Explore the applications of novel architectures (transformers, foundational models, etc.) for credit risk and related tasks
Collaborate with cross-functional teams to identify opportunities for data-driven insights and solutions
Work with other members of the Credit Risk team to build credit policies across the full customer lifecycle: origination, limit management, restructuring, and write-offs
Own at least one Machine Learning model end-to-end
Communicate findings and insights to technical and non-technical stakeholders