Senior ML Engineer

Game District; Punjab; Sur place; PKR 3 500 000 - 6 000 000; Plein temps

Résumé du poste

GameDistrict is hiring a Senior ML Engineer / Data Scientist to build machine learning models that help us understand our players and improve our games. You will own projects end to end, from problem framing and data exploration to production deployment and impact measurement.

You will work closely with product, marketing, and engineering teams, turning data into decisions across our mobile game portfolio. Experience: 4–5+ years in data science or machine learning, with models shipped to

Qualifications

• 4–5+ years hands-on data science or ML engineering experience.
• Experience shipping models to production.

Responsabilités

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

Connaissances

Python

Pandas

NumPy

Scikit-learn

XGBoost

LightGBM

CatBoost

SQL

ML Deployment

MLOps

A/B Testing

Communication

Formation

Bachelor’s or MSc in CS/Statistics/Math

Outils

Docker

Kubernetes

MLflow

Spark

Description du poste

Senior ML Engineer

Location: Lahore, Pakistan (On-site)
Experience: 4–5+ Years
Tech Stack: Python, SQL, Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, CatBoost, Cloud Data & ML Tools, MLOps

About the Role

GameDistrict is hiring a Senior ML Engineer / Data Scientist to build machine learning models that help us understand our players and improve our games. You will own projects end to end, from problem framing and data exploration to production deployment and impact measurement.

You will work closely with product, marketing, and engineering teams, turning data into decisions across our mobile game portfolio.

Experience: 4–5+ years in data science or machine learning, with models shipped to production.

What You’ll Do

• 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.

What You’ll Bring

• 4–5+ years of hands-on experience in data science or ML engineering, with at least 2 models you built running in production.
• Proven work on user-level predictive modeling, such as classification, regression, or time-to-event problems.
• Strong Python (Pandas, NumPy, Scikit-learn) and advanced SQL on large datasets.
• Deep experience with gradient-boosted trees (XGBoost, LightGBM, CatBoost) and solid grounding in statistics and probabilistic modeling.
• Sound understanding of model evaluation, calibration, class imbalance, leakage, and validation on time-based splits.
• Experience with ML deployment and MLOps: containers, workflow orchestration, experiment tracking, and model monitoring.
• Working knowledge of experimentation: A/B test design, statistical significance, and causal methods.
• Ability to explain technical results to non-technical stakeholders and tie them to business metrics.
• Bachelor’s or Master’s in Computer Science, Statistics, Mathematics, or a related field, or equivalent experience.

Nice to Have

• Experience in mobile gaming or other consumer apps.
• Experience with real-time feature stores and low-latency model serving.
• Exposure to deep learning for sequential or behavioral data.
• Experience with Spark or other distributed processing at scale.
• Publications, Kaggle results, or open-source contributions.