About the role
Responsibilities:
- Assist in developing and training machine learning models for credit scoring, fraud detection, and risk prediction.
- Perform data cleaning, preprocessing, and feature engineering on large datasets.
- Support model evaluation, experimentation, and optimization using various ML algorithms and validation techniques.
- Build and maintain automated pipelines for data processing, model training, and evaluation.
- Monitor model performance, track data drift, and identify production issues.
- Collaborate with data scientists, engineers, and product teams to deliver business-focused ML solutions.
Requirements:
- 1+ years of experience in machine learning or data science projects.
- Strong proficiency in Python with hands-on experience in: NumPy, Pandas, Scikit-learn, Matplotlib / Seaborn
- Experience in data preprocessing, feature engineering, and handling large datasets.
- Good understanding of ML concepts such as model evaluation, cross-validation, overfitting, and feature importance.
- Proficiency in SQL for data extraction and analysis.
- Familiarity with Git and collaborative development workflows.
- Strong analytical and problem-solving skills.
- Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a related field.
- Knowledge of fintech, credit risk, or business analytics domains.
- Basic understanding of MLOps, model deployment, and pipeline automation.
- Passion for continuous learning and exploring emerging technologies in machine learning and AI.