Senior ML Engineer — Production ML for Player Insights

Game District; Lahore; On-site; PKR 2,400,000 - 4,800,000; Full time

Job summary

GameDistrict in Lahore, Pakistan is seeking a Senior ML Engineer to lead end-to-end machine learning projects from problem framing to production deployment. You will work closely with product, marketing, and engineering teams to translate data insights into game-enhancing decisions and measurable impact.

The role requires 4–5+ years in data science or ML with models deployed in production, strong Python/SQL, and deep experience with gradient-boosted trees plus MLOps practices.

Qualifications

• 4–5+ years in data science or ML engineering with models in production.
• Strong Python and SQL on large datasets.
• Experience with gradient-boosted trees (XGBoost/LightGBM/CatBoost) and ML deployment/MLOps.
• Ability to frame business questions as ML problems and define success metrics.
• Experience communicating results to technical and non-technical stakeholders.

Responsibilities

• Design, build, and maintain predictive models on player behavior and engagement data.
• Frame business questions as ML problems and define success metrics with stakeholders.
• Engineer features from large-scale event and telemetry data.
• Take models to production: training pipelines, scoring, monitoring, and retraining.
• Design and analyze experiments to measure the impact of model-driven decisions.
• Communicate findings and recommendations clearly to technical and non-technical audiences.
• Mentor junior team members, review code and models, and help set ML best practices.

Skills

Python

SQL

Pandas

NumPy

Scikit-learn

XGBoost

LightGBM

CatBoost

MLOps

A/B testing

Education

Bachelor's or Master's in CS/Statistics/Math

Tools

Spark

Job description

GameDistrict in Lahore, Pakistan is seeking a Senior ML Engineer to lead end-to-end machine learning projects from problem framing to production deployment. You will work closely with product, marketing, and engineering teams to translate data insights into game-enhancing decisions and measurable impact.

The role requires 4–5+ years in data science or ML with models deployed in production, strong Python/SQL, and deep experience with gradient-boosted trees plus MLOps practices.