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Lead the development and enhancement of forecasting models, especially LSTM (Long Short-Term Memory) neural networks, to predict retail fuel prices using historical trends and market signals
Design, train, and review deep learning architectures in PyTorch, with scalable ML (machine learning) pipelines deployed via Databricks
Write and review production-grade Python code for feature engineering, model training, and seamless integration with downstream systems
Apply advanced ML techniques, including reinforcement learning and time-series modeling, to optimize pricing strategies in real time
Explore and apply Large Language Models (LLMs) and generative AI tools to accelerate feature documentation, scenario simulation, and pricing model summarization
Collaborate with pricing analysts, engineers, and product managers to define data science roadmaps, align solutions with business needs, and maintain model relevance
Run structured validation cycles, maintain experiment documentation, and contribute to the definition and improvement of model monitoring and fairness review frameworks
Mentor and guide junior data scientists by reviewing their work, helping scope projects, and supporting technical growth
Requirements:
Master’s or foreign equivalent degree in Data Science, Applied Data Science, Computer Science, Machine Learning, or a related field
2 years of experience in the job offered or as an AI or ML Engineer, Deep Learning Engineer, Data Scientist, or in a related/similar position
2 years in machine learning model development, using programming languages such as Python
1 year with deep learning frameworks such as PyTorch or TensorFlow, time series forecasting models such as LSTM or LLM, and Databricks or equivalent ML platform