Machine Learning And Scikit-learn Predictive Modeling Engineer

What role will you play in the team

As a Machine Learning and scikit-learn Predictive Modeling Engineer, you will spearhead the creation, training, and validation of statistical classification and regression models. Your core objective is to convert historical operational data into predictive algorithms that accurately forecast customer churn, inventory demand, and financial risk profiles.

What you will do

You will ingest large-scale datasets, engineer high-predictive-power features, train and tune various machine learning algorithms using scikit-learn, evaluate model performance metrics, and package these models into clean, deployable production-ready scripts for downstream enterprise consumption.

Key responsibility

• Design, implement, and optimize supervised and unsupervised machine learning algorithms using Python and scikit-learn.
• Perform extensive exploratory data analysis, handling missing values, outlier detection, and advanced feature engineering techniques.
• Conduct rigorous model evaluation using cross-validation, precision-recall metrics, ROC-AUC curves, and root mean squared error.
• Containerize machine learning workflows and collaborate with software engineering teams to integrate models into production applications.
• Monitor deployed model drift, data degradation, and retrain algorithms periodically to maintain peak predictive accuracy.
• Research state-of-the-art statistical modeling techniques and apply them to solve complex business domain challenges.

Required Qualification and Skills

• Bachelor's or Master's degree in Artificial Intelligence, Machine Learning, Computer Science, or Quantitative Analytics.
• 0 to 1 year of professional or academic experience building and deploying machine learning models.
• Expert-level proficiency in Python, utilizing libraries such as scikit-learn, NumPy, Pandas, SciPy, and Matplotlib.
• Solid understanding of statistical learning theory, feature scaling, dimensionality reduction, and hyperparameter tuning.
• Proficiency in SQL and relational database querying for extracting training datasets efficiently.
• Strong debugging skills and the ability to write modular, well-documented, and efficient object-oriented Python code.

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Benefits Included

• Competitive base salary complemented by performance-based quarterly milestone bonuses and equity options.
• Comprehensive health insurance covering hospitalization, outpatient care, and wellness checkups.
• Generous paid time off, maternity/paternity leave, and compassionate bereavement policies.
• Access to high-end computing workstations and cloud credits for continuous algorithmic experimentation.
• Structured mentorship program pairing junior engineers with principal data scientists and AI architects.

A Day in the Life

You start your morning by reviewing the evaluation metrics of a customer segmentation model trained overnight. You spend several hours engineering new interaction features and tuning hyperparameters using grid search in scikit-learn. Post-lunch, you participate in a sprint planning session with data engineers to discuss feature store integration requirements and document your model validation findings.