Sr. Machine Learning Engineer

We're working with a Telecom company that's using Machine Learning to answer a simple but critical business question:

❝ 𝗪𝗵𝗲𝗻 𝘄𝗶𝗹𝗹 𝘁𝗵𝗶𝘀 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗯𝗲 𝗿𝗲𝗮𝗱𝘆? ❞

The organization relies on predictive models to forecast outcomes such as project timelines, permit and approval cycles, resource planning, customer demand, and operational risks. These forecasts directly influence when marketing launches campaigns, when sales begin customer outreach, and how operations allocates resources.

The team already has models in production and is looking for someone who can take ownership, identify opportunities for improvement, engineer better features, optimize model performance, and increase prediction accuracy.

𝗪𝗵𝗮𝘁 𝘄𝗲'𝗿𝗲 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿:

Strong Python and SQL experience

Hands-on Machine Learning and Predictive Analytics background

XGBoost, Scikit-Learn, LightGBM, or similar algorithms

Forecasting and time-series modeling experience

Feature engineering and model optimization

Experience evaluating models using metrics such as Precision, Recall, F1 Score, ROC-AUC, MAE, RMSE, etc.

PySpark / Spark

MLflow, Docker, Kubernetes

AWS or Azure

Experience deploying and supporting production ML models

𝗜𝗱𝗲𝗮𝗹 𝗕𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱𝘀:

Demand Forecasting

Customer Churn Prediction

Revenue Forecasting

Risk Scoring

Resource Planning

Pricing Optimization

Capacity Planning

Supply Chain Analytics

Operational Forecasting

✅ 𝗪𝗲'𝗿𝗲 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝘄𝗵𝗼 𝗲𝗻𝗷𝗼𝘆𝘀 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴, 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲.

If you've improved production ML models and can clearly explain the business impact behind your predictions, we'd love to connect.

Interested? Send me your updated resume and the best time to reach you.