Asks for

The ad says“Good familiarity with deep learning concepts and frameworks (e.g., TensorFlow, PyTorch, or similar).”
The ad says“Strong knowledge of statistics, probability theory, and experimental design.”
The ad says“Deep understanding of core machine learning algorithms (supervised and unsupervised) and when to apply them.”
The ad says“Advanced Python skills, including strong knowledge of common data science libraries such as NumPy, Pandas, SciPy, scikit-learn, Matplotlib/Seaborn, and relevant time series libraries.”
The ad says“Advanced Python skills, including strong knowledge of common data science libraries such as NumPy, Pandas, SciPy, scikit-learn, Matplotlib/Seaborn, and relevant time series libraries.”
The ad says“Advanced Python skills, including strong knowledge of common data science libraries such as NumPy, Pandas, SciPy, scikit-learn, Matplotlib/Seaborn, and relevant time series libraries.”
The ad says“Strong knowledge of statistics, probability theory, and experimental design.”
The ad says“Advanced Python skills, including strong knowledge of common data science libraries such as NumPy, Pandas, SciPy, scikit-learn, Matplotlib/Seaborn, and relevant time series libraries.”
The ad says“Good familiarity with deep learning concepts and frameworks (e.g., TensorFlow, PyTorch, or similar).”
The ad says“Advanced Python skills, including strong knowledge of common data science libraries such as NumPy, Pandas, SciPy, scikit-learn, Matplotlib/Seaborn, and relevant time series libraries.”
The ad says“Advanced Python skills, including strong knowledge of common data science libraries such as NumPy, Pandas, SciPy, scikit-learn, Matplotlib/Seaborn, and relevant time series libraries.”
The ad says“Advanced Python skills, including strong knowledge of common data science libraries such as NumPy, Pandas, SciPy, scikit-learn, Matplotlib/Seaborn, and relevant time series libraries.”
The ad says“Experience with SQL and working with large-scale structured and unstructured datasets.”
The ad says“Strong knowledge of statistics, probability theory, and experimental design.”
The ad says“Good familiarity with deep learning concepts and frameworks (e.g., TensorFlow, PyTorch, or similar).”
The ad says“Solid expertise in time series forecasting and related evaluation methodologies.”

Who we are

Vorsee (by ZYTLYN Technologies) empowers companies across the $11 trillion travel industry to shape the future with predictive AI solutions that augment commercial planning, sales, marketing, retailing and operations. We work with some of the largest travel brands in the world, and our vision is to answer highly detailed and granular questions about the future of travel, such as demand, supply, market fluctuations and pricing. Our core focus is on airlines, airports, travel agencies, destinations, tourism boards, hotels, car rentals, travel retailers, and luxury brands.

Who we are looking for

We are looking for a Senior Data Scientist who is passionate about solving complex data and forecasting problems in the travel industry. You are someone who thrives on turning large-scale, real-world data into accurate, actionable insights and predictions that drive business decisions. You combine strong statistical foundations with practical machine learning expertise and are comfortable owning projects end-to-end from problem framing and data exploration to deployment and monitoring in a cloud environment.

You are curious, business-oriented, and collaborative. You understand that forecasting in the travel industry requires both technical excellence and domain awareness, especially when working with seasonality, demand volatility, and external drivers.

Location / Contract type

Full remote, or Geneva, Switzerland Office;
Full-time, b2b Contractor, or Swiss employment contract.

Our culture

We have a culture that focuses on empowering people, with team members working in our HQ (Geneva, Switzerland), and all across Europe (e.g. France, Spain, Italy, Poland, UK). We believe a diverse team creates better outcomes and fosters a better environment for learning and growth. We put a lot of emphasis on communication, listening, efficient processes and trusting our team. We rely on each other, and work together to achieve our common goals. We believe in working smart, with strong focus and intensity, tackling every challenge as a team.

Your work

As a Senior Data Scientist, you will:

Design, develop, and maintain time series forecasting models to predict travel demand, pricing dynamics, and related KPIs.
Work with large-scale datasets (e.g., data pipelines, model training).
Apply and compare classical statistical models (e.g., ARIMA/SARIMA, ETS, Prophet) and machine learning models (e.g., Random Forest, Gradient Boosting, XGBoost, LightGBM) for forecasting tasks.
Explore and implement deep learning approaches where appropriate.
Perform thorough data analysis, feature engineering, and validation tailored to time series data (cross-validation strategies, backtesting, handling seasonality and trends).
Collaborate with data engineers, product managers, and business stakeholders to translate business needs into scalable data solutions.
Ensure high-quality code standards, reproducibility, and documentation.
Contribute to model deployment and lifecycle management in production environments.

Requirements

Basic requirements

Strong experience (5+ years) in Data Science or Machine Learning roles.
Solid expertise in time series forecasting and related evaluation methodologies.
Deep understanding of core machine learning algorithms (supervised and unsupervised) and when to apply them.
Good familiarity with deep learning concepts and frameworks (e.g., TensorFlow, PyTorch, or similar).
Advanced Python skills, including strong knowledge of common data science libraries such as NumPy, Pandas, SciPy, scikit-learn, Matplotlib/Seaborn, and relevant time series libraries.
Strong knowledge of statistics, probability theory, and experimental design.
Experience with SQL and working with large-scale structured and unstructured datasets.
Ability to write clean, maintainable, and production-ready code.
Strong communication skills and the ability to explain complex technical concepts to non-technical stakeholders.
Resourceful self-starter, comfortable with ambiguity and shifting priorities in a startup;
Highly organised, disciplined, and detail-oriented;

Bonus points

Experience in the travel industry or similar demand-driven industries.
Knowledge of MLOps practices and tools.
Experience with embeddings, vector databases, hybrid search, chunking, reranking, tool-calling, and source-grounded LLM answers.
Python/SQL skills and experience building scalable pipelines for large datasets, APIs, databases, indexing, data quality, access control, and production cloud deployment.
Experience working in AWS cloud environments (e.g., S3, EC2, Lambda, or similar services).
Experience with containerisation and orchestration technologies such as Docker and Kubernetes.
Experience building and maintaining CI/CD pipelines for ML workflows.
Familiarity with model monitoring, drift detection, and automated retraining strategies.
Contributions to open-source projects or published work in forecasting or machine learning.

Benefits

What we offer

Join a team of exceptional talent — At Vorsee, we hire thoughtfully and selectively, bringing together a small, focused team of high performers. We believe that a lean and empowered team moves faster, builds smarter, and achieves more. You’ll collaborate with driven colleagues who value efficiency, ownership, and impact.
Competitive salary- adjusted for experience and market benchmarks.