Data Scientist - Deep Learning Forecasting (Demand Estimation Team)

Data Scientist - Deep Learning Based Demand Forecasting

Fetcherr experts in deep learning, e-commerce, and digitization, Fetcherr disrupts traditional systems with its cutting-edge AI technology. At its core is the Large Market Model (LMM), an adaptable AI engine that forecasts demand and market trends with precision, empowering real-time decision-making. Specializing initially in the airline industry, Fetcherr aims to revolutionize industries with dynamic AI-driven solutions.

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, and backend) to deploy ML systems in production.
• Mentor junior team members and promote best practices across modeling, experimentation, and code quality.
• Clearly communicate complex technical findings to non-technical stakeholders, including product leaders and executives.
  
  

• 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, SQL).
• Expertise in time-series forecasting, ideally Deep Learning based, preferably in demand prediction or related areas.
• Domain expertise in revenue management related pipelines, domains, problems.
• Feature engineering, feature importance testing, per sample explainability based experience.
• Master’s or PhD in Computer Science, Machine Learning, Statistics, Engineering or a relevant field.
• Publications in top-tier, peer-reviewed ML/AI venues (e.g. ICLR, ICML, NIPS, etc.)
• Solid understanding of ML production workflows (versioning, testing, reproducibility, and deployment).
• Excellent communication and collaboration skills.
  
  

• Experience applying ML in domains like finance, trading, reinforcement learning, or NLP.
• 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).