Junior Python And Scikit-learn Machine Learning Engineer

What role will you play in the team

You will serve as an integral pillar of our core data science division, focusing on the end-to-end development of predictive modeling solutions. As our Junior Python and scikit-learn Machine Learning Engineer, you will take ownership of transforming exploratory algorithms into production-ready predictive systems that forecast customer churn, optimize pricing structures, and automate critical business classification tasks for our high-growth enterprise clients.

What you will do

You will write modular Python code to preprocess large datasets, engineer robust predictive features, train classification and regression models using scikit-learn, and evaluate model performance using cross-validation metrics. You will also containerize models for deployment, monitor drift in production environments, and work alongside senior data scientists to integrate machine learning pipelines into existing software microservices.

Key responsibility

• Develop, train, and fine-tune supervised and unsupervised machine learning algorithms using Python and scikit-learn libraries.
• Perform extensive exploratory data analysis, outlier detection, missing value imputation, and advanced feature engineering on raw datasets.
• Evaluate model accuracy, precision, recall, F1-score, and ROC-AUC metrics to ensure optimal generalization on unseen test data.
• Collaborate with backend software engineering teams to deploy machine learning models via RESTful APIs and microservices.
• Conduct rigorous hyperparameter tuning using grid search and randomized search techniques to maximize predictive performance.
• Maintain comprehensive documentation of experimental setups, feature pipelines, version-controlled model artifacts, and evaluation metrics.

Required Qualification and Skills

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or Applied Mathematics.
• Minimum 1 to 2 years of professional or rigorous academic experience building machine learning models in Python.
• Strong working knowledge of data science libraries including NumPy, Pandas, scikit-learn, and Matplotlib.
• Solid foundational understanding of statistical analysis, probability distributions, linear algebra, and calculus.
• Familiarity with relational databases, SQL querying, and data extraction pipelines from MongoDB or PostgreSQL.
• Strong debugging skills, version control proficiency using Git, and collaborative problem-solving capabilities.

Benefits Included

• Attractive annual salary package with performance-based quarterly incentives and financial bonuses.
• Comprehensive health, dental, and vision insurance coverage for employees and immediate family members.
• Work From Office setup featuring ergonomic workstations, high-speed fiber connectivity, and collaborative recreational spaces.
• Sponsored access to advanced machine learning specializations, academic conferences, and certification programs.
• Generous paid time off, mental health wellness days, and structured corporate team-building events.

A Day in the Life

You kick off your morning by analyzing the training logs of a gradient boosting classifier you set up to run overnight. After identifying a slight overfitting issue, you spend a few hours modifying the feature selection pipeline and running cross-validation tests in a Jupyter notebook. Post-lunch, you sync with the data engineering squad to discuss schema adjustments for incoming telemetry streams before pushing your code to the staging repository.