This role sits at the core of a high-impact fintech environment focused on building and optimizing credit and risk intelligence systems at scale. You will work on advanced machine learning models that directly influence credit decisions, transaction risk evaluation, and user onboarding strategies across millions of users. Operating in a fast-paced, AI-first and fully remote setup, you will help shape the evolution of data-driven lending products in a rapidly growing financial ecosystem. The position combines deep technical expertise with strong business impact, requiring both analytical rigor and product-oriented thinking. You will also contribute to modernizing data infrastructure and driving the adoption of next-generation tools and methodologies. This is a highly influential role where your work directly improves financial access and decision-making for millions of users.
Accountabilities:
* Develop and deploy real-time scoring models to assess credit and transaction risk with high accuracy and scalability.
* Build predictive machine learning models using internal and external data to optimize onboarding and reduce credit losses.
* Evolve static rule-based systems into dynamic, graph-based decision engines for more adaptive risk management.
* Analyze large-scale behavioral, transactional, and demographic datasets to identify patterns and improve risk strategies.
* Design and implement advanced solutions such as fingerprinting and geolocation-based tracking to enhance fraud and risk detection.
* Monitor, evaluate, and improve the performance, stability, and accuracy of credit and risk models in production environments.
* Lead technical initiatives, mentor team members, and contribute to best practices in data science and machine learning engineering.
* Support the adoption of modern data platforms and tools, including Databricks and data catalog systems, to improve scalability and efficiency.
Requirements:
* Strong proficiency in Python, SQL, and Spark, with experience using libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib.
* Solid experience building and deploying machine learning models, including classification, regression, clustering, and feature engineering.
* Deep understanding of model evaluation techniques such as AUC, KS, precision, recall, and statistical significance testing.
* Strong background in A/B testing, experimental design, and statistical analysis.
* Experience working with large-scale datasets and writing optimized SQL queries for performance.
* Knowledge of model interpretability techniques such as SHAP, weight of evidence, and bivariate analysis.
* Familiarity with cloud and data platforms such as Databricks, AWS, or Azure, and version control tools like Git.
* Strong problem-solving mindset with the ability to translate complex technical results into actionable business insights.
* Excellent communication and collaboration skills in cross-functional and technical environments.
* Nice to have: experience with PySpark, MLOps, production model monitoring, and fintech or credit-related data systems.
Benefits:
1. Fully remote role, allowing you to work from anywhere in the world.
2. Competitive and market-aligned compensation package.
3. Home office allowance provided via monthly deposit.
4. Comprehensive health and dental insurance with no co-