Data Scientist - Deep Learning Forecasting (Demand Estimation Team)

Department
AI
Type
Full-time
Level
Location
Israel
About The Position

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 Scientistto 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.

About The Position

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, 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.

Nice to have:

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).

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.