Data Scientist

Who We Are ZYTLYN Technologies aids companies across the travel industries to forecast the future with predictive insights, by enhancing commercial planning and operations. We produce predictive travel solutions to improve the decision-making process of companies. We are currently seeking a data scientist to join our existing team. You will collaborate in a dynamic team consisting of data scientists, and data and machine learning engineers. Location The positions available are in our Geneva/Switzerland Office, Hybrid, or Fully remote positions. You need to be located between GMT+1 and GMT+4. Culture We are remote first, with team members functioning all across Europe (Switzerland, France, Spain, Italy, Poland). We believe that a diverse team operates better. We focus greatly on communication and on empowering and trusting our team. We depend on each other and collaborate to achieve our shared objectives. We believe in working smart and efficiently and facing every challenge as a team, every voice is heard, and every voice matters. Our Stack For project management, we use Jira, Confluence, Slack, Google Workspace. We work with Python, Tensorflow, Scikit-learn, Numpy, Scipy, and Pandas. We also use Kubernetes, AWS: Lambda, EC2, EKS, Athena, Glue, S3, SQS, Docker and Terraform, and Git/CICD: Gitlab. Your work You will work with the Data Science and ML engineering team in constructing, maintaining, and optimising the ZYTLYN Prediction Platform. This includes: Proposing and designing relevant features from data. Understanding and addressing model performance bottlenecks. Discussing training and modeling strategies with the team. Fine-tuning model hyperparameters and architecture for optimal performance. Experimenting with novel architectures. Requirements Your Skills You should have a strong foundation in statistics and familiarity with a wide range of machine learning algorithms. Proficiency in Python for data manipulation, preprocessing, and model implementation. Familiarity with Linux OS is a significant plus. Strong understanding of deep learning architectures, including LSTM, GRU, and attention mechanisms. Experience with frameworks such as TensorFlow or PyTorch for building and training deep learning models. Significant knowledge of model optimization techniques to reduce model size, memory use, and inference time. Effective development practice: Merge Request, CICD, Test, GitHub/GitLab Flow. You should have the capability to explain complex technical concepts to non-technical stakeholders. You should demonstrate enthusiasm for staying updated with the latest advancements in deep learning and time series forecasting techniques. Strong communication skills for presenting results, insights, and recommendations. You should be a team player and collaborative. English: professional working proficiency. Benefits Compensation Between CHF 55’000 and CHF 75’000 depending on profile.