Data Scientist - Deep Learning Forecasting (CBM 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 Data Scientist to help advance our machine learning capabilities. The ideal candidate is a motivated, independent thinker with a passion for using machine learning and data to drive 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.
  
  

• 3+ years of hands-on experience in data science and machine learning.
• Proven experience developing and deploying production-grade machine learning models.
• Degree in Computer Science, Machine Learning, Statistics, or related field.
• Expertise in time-series forecasting, preferably in demand prediction or related areas.
• Proficiency in Python and its ML/data stack (e.g., PyTorch or TensorFlow, Pandas, NumPy, Scikit-learn).
• Solid understanding of ML production workflows (versioning, testing, reproducibility, and deployment).
• Excellent communication and collaboration skills.
• Strong data visualization and exploratory data analysis skills.
• Background in code optimization, containerization (e.g., Docker), CI/CD, or cloud-native architectures.
  
  

• Master’s or PhD in Computer Science, Machine Learning, Statistics, or a related field.
• Publication(s) in a top-tier, peer-reviewed ML/AI venue (e.g., NeurIPS, ICML, ICLR, AAAI).
• Experience applying ML in domains like finance, trading, reinforcement learning, or NLP.
• Familiarity with MLOps tooling and cloud platforms (e.g., GCP, AWS).
• Experience with workflow orchestration tools like Apache Airflow or Dagster.
• Participation in competitive programming or data science challenges (e.g., Kaggle).