Senior Data Scientist

🚀 Welcome to Your Next Adventure! 🛠 What You’ll Get Your Hands On: 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. The Magic You Bring 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) Inside Snoonu’s…