Data Scientist - Deep Learning Forecasting

Fetcherr; Wrocław; On-site; PLN 240,000 - 360,000; Full time

Job summary

Fetcherr is seeking a talented and self-driven Data Scientist to advance our ML capabilities in a fast-growing AI company. You will build state-of-the-art econometric and forecasting models that translate market signals into actionable insights for commercial teams.

Collaborating with product, data engineering, MLOps and platform teams, you will deploy models to production and clearly communicate complex findings to non-technical stakeholders and executives.

Qualifications

• 5+ years hands-on data science and ML experience with a proven record of leveraging modeling into business outcomes.
• Proficiency in Python and ML 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.
• 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).

Responsibilities

• Develop and implement 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.

Skills

Data science

Machine learning

Time-series forecasting

Python

Communication

Education

Master’s or PhD in Computer Science, ML, Statistics, Engineering or relevant field

Tools

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Job 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

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 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, 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).