Temel

Senior Data Scientist

Geneva, Switzerland Şirket: TN Switzerland Müşteri / İşveren: Vorsee
Yayınlandı: 02.06.2026
Kapanış tarihi: 17.07.2026
İş referansı: cb45e7b7cc6253ae9547197b661af6eb

İş bilgileri

Konum
Geneva, Switzerland
Şirket
TN Switzerland
Müşteri / İşveren
Vorsee
İş referansı
cb45e7b7cc6253ae9547197b661af6eb
Listeleme türü
Temel
AB çalışma izni zorunlu
Hayır
Yayınlandı
02.06.2026
Kapanış tarihi
17.07.2026

İş tanımı

Who we areVorsee (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 forWe 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 typeGeneva, Switzerland Office;Full-time, Permanent contract.Our cultureWe 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 workAs 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.RequirementsBasic requirementsStrong 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 pointsExperience 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.BenefitsWhat we offerJoin 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.

Yetenekler

apply blended learning apply for research funding apply research ethics and scientific integrity principles in research activities build recommender systems Business Analytics Business Intelligence collect ICT data communicate with a non-scientific audience Computational Biology Computer Simulation conduct research across disciplines create data models Data Engineering data ethics Data Mining Data Models data quality assessment Data Science data visualisation software define data quality criteria deliver visual presentation of data demonstrate disciplinary expertise design database in the cloud design database scheme develop data processing applications develop professional network with researchers and scientists Digital Curation disseminate results to the scientific community draft scientific or academic papers and technical documentation empirical analysis establish data processes evaluate research activities execute analytical mathematical calculations Hadoop handle data samples Healthcare Analytics image recognition implement data quality processes increase the impact of science on policy and society information categorisation Information Extraction integrate gender dimension in research integrate ICT data interact professionally in research and professional environments interpret current data LDAP LINQ make data-driven decisions manage data manage data collection systems manage findable accessible interoperable and reusable data manage ICT data architecture manage ICT data classification manage intellectual property rights manage open publications manage personal professional development manage research data Marketing Analytics mathematical modelling MDX mentor individuals multidisciplinary research N1QL normalise data online analytical processing operate open source software perform data cleansing perform data mining perform project management perform scientific research promote open innovation in research promote the participation of citizens in scientific and research activities promote the transfer of knowledge publish academic research quantitative analysis query languages report analysis results Research Design resource description framework query language Scientific Computing scientific literature Social Network Analysis SPARQL speak different languages State Estimation statistical modeling techniques Statistics synthesise information teach in academic or vocational contexts think abstractly Unstructured Data use data processing techniques use databases use spreadsheets software visual presentation techniques write scientific publications XQuery

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