Data Scientist - Deep Learning Forecasting

Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. The company's core product is the Market Model, a proprietary AI-powered model delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence. The Large Market Modeling (LMM) team develops and trains these models to convert complex market signals into reliable demand forecasts for pricing intelligence. Fetcherr serves global aviation clients and other volatile markets, delivering an average profit uplift of 7%.

• Over 5 years of hands-on experience in data science and machine learning with proven business impact.
• Proficiency in Python and its ML/data stack including PyTorch, TensorFlow, Pandas, NumPy, Scikit-learn.
• Expertise in time-series forecasting, ideally using deep learning, preferably in demand prediction.
• Experience in feature engineering, feature importance testing, and explainability.
• Master’s or PhD degree in Computer Science, Machine Learning, Statistics, Engineering, or related fields.
• Solid understanding of ML production workflows like versioning, testing, reproducibility, and deployment.
• Excellent communication and collaboration skills.

Nice to have:

• Publications in top-tier peer-reviewed ML/AI venues (e.g., ICLR, ICML, NIPS).
• Experience applying ML in finance, trading, or revenue management.
• Familiarity with GCP cloud solutions such as Vertex AI, PubSub, Cloud Run Functions.
• Strong data visualization and exploratory data analysis skills.
• Familiarity with code optimization, containerization (e.g., Docker), CI/CD, and cloud-native architectures.
• Participation in competitive programming or data science challenges (e.g., Kaggle).

• Develop and implement state-of-the-art econometric and machine learning models for demand forecasting.
• Conduct research and experimentation to evaluate novel approaches for improving accuracy, robustness, and scalability.
• Collaborate with cross-functional teams including product, data engineering, MLOPS, and Platform to deploy ML systems in production.
• Clearly communicate complex technical findings to non-technical stakeholders, such as product leaders and executives.

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