Kinetic Machine Learning Engineer

Uniti

1d•Remote

About The Position

Kinetic is seeking a Machine Learning Engineer with strong statistical foundations and hands-on modeling experience. The role involves building, maintaining, and improving predictive models across various business domains such as customer retention, network performance, marketing, and sales. The engineer will develop features from complex multi-source data, maintain inherited models, and contribute to all phases of the modeling lifecycle, from data exploration to model delivery. This position reports to the team manager and collaborates with data engineers and solutions architects. The role can be filled remotely anywhere within the country.

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

Nice To Haves

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

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