Junior Data Scientist – Machine Learning

As a Junior Data Scientist – Machine Learning you will work closely with senior data scientists and engineers to develop train and evaluate machine learning models for credit risk analysis fraud detection and customer onboarding solutions.
You will contribute to building scalable ML pipelines perform feature engineering and help deploy models that power fintech decision systems used by leading digital lenders.

Key Responsibilities
Model Development: Assist in developing and training machine learning models for credit scoring fraud detection and risk prediction using structured and alternate data sources.
Data Preparation & Feature Engineering: Work with large datasets to clean preprocess and engineer meaningful features to improve model performance.
Model Evaluation: Support experimentation with different algorithms hyperparameters and validation techniques to improve predictive accuracy.
Pipeline Development: Help build and maintain automated pipelines for data preprocessing model training and evaluation.
Model Monitoring: Assist in monitoring model performance tracking data drift and identifying potential issues in production systems.
Collaboration: Work closely with data scientists engineers and product teams to translate business requirements into ML solutions.

Required Skills and Experience
Machine Learning Fundamentals: 1 year of experience working with machine learning algorithms for classification or regression problems.
Python Programming: Strong Python skills with hands-on experience using libraries such as:
NumPy
Pandas
Scikit-learn
Matplotlib / Seaborn
Data Handling: Experience working with datasets for cleaning preprocessing and feature engineering.
SQL Knowledge: Ability to write SQL queries to extract and manipulate data.
ML Concepts: Understanding of key concepts such as:
model evaluation
cross-validation
overfitting
feature importance
Version Control: Familiarity with Git for collaborative development.
Problem Solving: Strong analytical thinking and ability to approach business problems using data.

Preferred Qualifications:
Education: A Bachelor’s or Master’s degree in Computer Science Data Science Mathematics or a related quantitative field.
Domain Knowledge: Familiarity with fintech credit risk or business analytics domains.
Automation & MLOps: Basic understanding of model deployment monitoring or pipeline automation tools.
Continuous Learning: Enthusiasm for exploring new ML algorithms open-source tools and emerging technologies in data science.

Required Skills:

Machine learning algorithms Classification Regression problems Python NumPy Pandas Scikit-learn Matplotlib Seaborn Data cleaning Data preprocessing Feature engineering SQL Model evaluation Cross-validation Overfitting Feature importance Git Analytical thinking Business problem solving Model deployment Model monitoring Pipeline automation