Kinetic Machine Learning Engineer

Windstream is a premier provider of multi-gigabit fiber internet and related services. They are seeking a Kinetic Machine Learning Engineer to build and maintain predictive models across various business domains, collaborating with data engineers and solutions architects to enhance model delivery and performance.

Responsibilities:

• Build and evaluate predictive models (logistic regression, XGBoost, ensemble methods) across customer retention, network, marketing, sales, and other business domains
• Engineer features from complex, multi-source enterprise data (billing systems, call center logs, CRM, network data) in Snowflake and Oracle
• Profile and investigate data quality issues — identify leakage, missingness patterns, join inconsistencies, and source-of-truth conflicts
• Maintain and improve inherited production models, including models built in Snowpark by external partners
• Perform SHAP-based model interpretability analysis and translate results into business-actionable insights
• Design and execute customer segmentation using clustering techniques on model outputs
• Write clear, thorough documentation of model logic, feature rationale, data assumptions, and known limitations
• Collaborate with the team to define target variables, population filters, and prediction windows grounded in statistical reasoning

Requirements:

• 2–3 years of experience in a data science or applied statistics role (less experience considered for strong candidates)
• Strong foundation in statistics: hypothesis testing, regression, classification, probability, bias-variance tradeoffs
• Proficiency in Python for data science (Pandas, scikit-learn, NumPy, Matplotlib/Seaborn)
• Strong SQL skills, particularly with Snowflake or similar cloud data warehouses
• Experience with feature engineering from real-world, imperfect enterprise data — not just clean Kaggle datasets
• Ability to work independently and manage your own priorities with minimal oversight
• Clear written and verbal communication — you can explain a modeling decision to a non-technical stakeholder and document your work so others can follow it
• Experience with Snowpark (Python or SQL)
• Exposure to Azure ML or similar cloud ML platforms
• Familiarity with MLOps concepts (model versioning, pipeline automation, drift monitoring)
• Telecom or subscription-based industry experience
• Experience inheriting and maintaining models built by others
• Familiarity with Git-based workflows and version control for data science artifacts