Machine Learning Engineer

About the role

We’re looking for a curious and impact‑driven Machine Learning Engineer to join our team and accelerate the development of robust, high‑performing ML solutions across key business areas such as credit scoring, collection, antifraud, and sales. The role blends hands‑on development with strategic input, allowing you to work on high‑visibility projects from day one while collaborating with multiple departments to deliver measurable business impact.

Key responsibilities

• Own the end‑to‑end lifecycle of ML models, from exploratory data analysis and feature generation to hypothesis testing, production deployment, and monitoring.
• Collaborate with cross‑functional teams—especially Collection and Product—to identify high‑leverage modeling opportunities.
• Evaluate model performance and business impact, iterating continuously for improvement.
• Explore and integrate new internal and external data sources to uncover additional predictive signals.
• Work with production systems to ensure models are scalable, observable, and maintainable.

Required profile

• 2+ years of experience as a Machine Learning Engineer or Data Scientist, preferably in fintech or fast‑paced product environments.
• Solid foundation in classical ML techniques and fluency with libraries such as Pandas, Scikit‑learn, NumPy, SciPy, Matplotlib, Seaborn, Statsmodels, XGBoost, LightGBM, and CatBoost.
• Strong proficiency in Python and SQL, with experience using version control systems.
• Strong sense of ownership and accountability.
• Experience with AWS or other cloud environments is a plus.

Required skills

• Python
• SQL
• Pandas
• Scikit‑learn
• NumPy
• SciPy
• Matplotlib
• Seaborn
• Statsmodels
• XGBoost
• LightGBM
• CatBoost
• AWS
• Git (version control)