Data Scientist - Logistics

Location: Remote, United States

Department: Data Science

Experience: 5+

• Lead the research, development, and implementation of advanced time series forecasting models to optimise logistics operations, including ETA prediction, preparation times, and demand forecasting.
• Design and implement optimization algorithms and solvers to improve logistics network efficiency, route planning, and resource allocation.
• Design and conduct rigorous experiments to test, validate, and benchmark models under various real-world scenarios, ensuring robustness and accuracy.
• Fine-tune, deploy, and monitor ML models and applications in production, maintaining retraining pipelines and ensuring model performance over time.
• Collaborate with ML engineers, backend engineers, researchers, and product engineers in a cross-functional setting to identify key operational challenges and develop innovative data-driven solutions.
• Conduct A/B tests and other experimentation techniques to evaluate model impact and drive continuous improvement across logistics KPIs.
• Translate complex analytical results into actionable business insights, presenting findings to senior leadership and stakeholders.
• Provide technical leadership in data science, mentoring junior team members and driving best practices in modelling, experimentation, and code quality.
• Stay up-to-date with the latest advancements in AI, ML, and operations research, integrating cutting-edge techniques into logistics solutions.

• MSc in Data Science, Statistics, Applied Mathematics, Computer Science, or a related field.
• Nice to Have: PhD in Data Science, Statistics, Computer Science, Electrical Engineering, or equivalent industrial experience.
• 5+ years of experience in data science, machine learning, or statistical modelling, with a strong focus on time series forecasting and/or optimisation problems.
• Extensive hands-on experience with time series forecasting methods (e.g., ARIMA, Prophet, LightGBM, TFT, DeepAR, N-BEATS) and regression/classification models.
• Practical experience with optimisation techniques and solvers (e.g., linear programming, mixed-integer programming, heuristic methods, OR-Tools, PuLP, or similar).
• Proficiency in Python, including libraries such as Pandas, NumPy, Scikit-learn, Optuna, LightGBM, TensorFlow, or PyTorch.
• Practical experience in developing and deploying ML models at scale, including monitoring and retraining pipelines.
• Practical experience in A/B testing and other experimentation techniques.
• Strong experience handling large datasets and proficiency with SQL databases (BigQuery, Redash, or others).
• Experience with cloud platforms such as AWS (ECS, S3, Lambda, Step Functions), GCP (BigQuery, VertexAI), and/or Databricks for ML model development and deployment.
• Proven experience in the delivery and logistics industry, with a strong understanding of its operational challenges and optimisation opportunities (a plus)