Data Scientist - Deep Learning Forecasting

Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. At the core of the company is the Market Model - a proprietary AI-powered model delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture, it uses market data - not personal data - with full transparency into logic and outcomes. First deployed in global aviation, the technology is industry-agnostic and scales across volatile markets. Fetcherr delivers a consistent average profit uplift of 7%, with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul.

We are seeking a talented and self-driven experienced Data Scientist to help advance our machine learning capabilities.This is a key role for someone passionate about leveraging machine learning to solve complex, real-world problems and deliver measurable business impact.

Responsibilities:

• 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, including product leaders and executives.

Requirements:

You’ll be a great fit if you have...

• 5+ years of hands-on experience in data science and machine learning with a proven record of leveraging modeling into business outcomes.
• Proficiency in Python and its ML/data stack (e.g., PyTorch or TensorFlow, Pandas, NumPy, Scikit-learn).
• Expertise in time-series forecasting, ideally Deep Learning based, preferably in demand prediction or related areas.
• Feature engineering, feature importance testing, explainability based experience.
• Master’s or PhD in Computer Science, Machine Learning, Statistics, Engineering or a relevant field.
• Solid understanding of ML production workflows (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, etc.)
• Experience applying ML in domains like finance, trading, revenue management etc.
• Familiarity with cloud based solutions on GCP platform (e.g., Vertex AI, PubSub, Cloud Run Functions).
• Strong data visualization and exploratory data analysis skills.
• Familiarity with code optimization, containerization (e.g., Docker), CI/CD, or cloud-native architectures.
• Participation in competitive programming or data science challenges (e.g., Kaggle).

If you're excited about building impactful AI systems in a high-growth startup environment, and want to help redefine how industries price, forecast, and optimize, we’d love to hear from you.