Data Scientist - Deep Learning Forecasting
Confirmed on the employer's own hiring board on Oct 14, 2026. First seen by Alion on Oct 4, 2026.
Description
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.
The Large Market Modeling (LMM) team is the engine underneath Fetcherr's pricing intelligence. We're the ones who actually build and train the models - taking a chaotic world of market signals, customer behavior, and competitive dynamics and turning them into reliable, production-ready demand models. Other teams at Fetcherr work with the models; we're the ones who bring them to life. Think of us as the team that teaches Fetcherr's AI how people buy - so it can always recommend the right price at the right moment.
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
- 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.