Senior Data Scientist: Logistics Forecasting & Optimization

سنونو; Doha; On-site; QAR 350,000 - 520,000; Full time

Benefits offered by this job

Global collaboration

Learning budgets

Autonomy & ownership

Flexible time off

Job summary

Snoonu in Qatar is seeking a data science leader to research, develop, and deploy advanced forecasting models for logistics, improving ETA accuracy and demand planning. You will design optimization solvers and run rigorous experiments to ensure robustness, partnering with ML and backend engineers to deliver scalable data-driven solutions.

The role emphasizes production deployment, monitoring, and continuous improvement, with a strong focus on measuring impact on logistics KPIs and communicating

Qualifications

• Advanced degree in a quantitative field with strong mathematical grounding.
• 5+ years in data science, ML, or statistical modelling with time series focus.
• Proven ability to deliver models from research to production with monitoring.

Responsibilities

• Lead R&D of time series forecasting and optimization models for logistics.
• Design experiments, benchmarks, and ablation studies to prove value.
• Deploy, monitor, and retrain ML models in production environments.
• Collaborate with engineers and product teams to scale solutions.
• Translate results into business insights for senior stakeholders.

Skills

Time series forecasting

Optimization

Experimentation

Data storytelling

Leadership

Python programming

SQL

Cloud platforms

Education

MSc in Data Science or related field

PhD (nice to have)

Tools

Python

Pandas

NumPy

Scikit-learn

Optuna

LightGBM

TensorFlow/PyTorch

SQL/BigQuery

AWS/GCP/Databricks

Job description

Snoonu in Qatar is seeking a data science leader to research, develop, and deploy advanced forecasting models for logistics, improving ETA accuracy and demand planning. You will design optimization solvers and run rigorous experiments to ensure robustness, partnering with ML and backend engineers to deliver scalable data-driven solutions.

The role emphasizes production deployment, monitoring, and continuous improvement, with a strong focus on measuring impact on logistics KPIs and communicating