Senior Data Scientist

Role overview

Responsibilities

• Lead development and implementation of forecasting models for ETAs, preparation times, and demand.
• Design optimization algorithms and solvers for logistics networks, route planning, and resource allocation.
• Design experiments to validate and benchmark models in real-world scenarios.
• Deploy, monitor, and maintain production ML models, applications, and retraining pipelines.
• Collaborate cross-functionally to solve operational challenges with data-driven solutions.
• Run A/B tests to evaluate model impact and improve logistics KPIs.
• Present actionable analytical insights to senior leadership and stakeholders.
• Mentor junior team members and drive modelling, experimentation, and code-quality practices.
• Track AI, ML, and operations research advances and apply relevant techniques to logistics.

Qualifications and experience

• MSc in Data Science, Statistics, Applied Mathematics, Computer Science, or a related field.
• PhD in Data Science, Statistics, Computer Science, Electrical Engineering, or equivalent industrial experience is nice to have.
• 5+ years of data science, machine learning, or statistical modelling experience focused on forecasting and/or optimization.
• Hands-on experience with time series forecasting and regression or classification models.
• Practical experience with optimization techniques and solvers.
• Proficiency in Python and relevant libraries; production ML deployment, monitoring, and retraining experience.
• Practical A/B testing and experimentation experience; strong large-dataset and SQL skills.
• Experience with AWS, GCP, and/or Databricks for ML development and deployment.
• Delivery and logistics industry experience is a plus.

Examples of methods and tools

• Forecasting examples include ARIMA, Prophet, LightGBM, TFT, DeepAR, and N-BEATS.
• Optimization examples include linear programming, mixed-integer programming, heuristics, OR-Tools, and PuLP.
• Python library examples include Pandas, NumPy, Scikit-learn, Optuna, LightGBM, TensorFlow, and PyTorch.
• Cloud examples include AWS ECS, S3, Lambda, and Step Functions; GCP BigQuery and Vertex AI; and Databricks.

About Snoonu

Perks and workplace culture

• The posting mentions global collaboration, learning budgets and courses, autonomy, generous leave, wellness policies, and agile ways of working.
• Snoonu states that it is Great Place to Work certified and holds ISO 9001:2015 and ISO 45001:2018 certifications.
• The company describes its culture as inclusive and community-minded.