Data Scientist

Location: Basel, Switzerland Contract Duration: 3 years, with possible extension Experience Required: 3+ years. Industry Experience: Utilities & Energy ( Energie & Wasserversorgung) or Telecommunications or Banking & Financial Services or Information Technology & Services Role Overview

We are looking for an experienced Data Scientist to support a long-term enterprise project in Basel. This is a 3-year contract role with the possibility of extension. We are seeking a senior-tier Data Engineer to design, build, and optimize our enterprise-wide data platform. In this role, you will be responsible for breaking down operational data silos and constructing scalable, low-latency ETL/ELT pipelines. Your mission is to establish a unified data architecture that delivers clean, highly structured, and reliable data streams for downstream business intelligence, advanced analytics, and AI initiatives. Strategic Scope: Formulates, trains, validates, and deploys scalable statistical analysis models, machine learning systems, and artificial intelligence pipelines to extract actionable predictive trends and enable smart automation systems. Core Responsibilities

• Problem Translation: Translate high-level operational and business challenges into precise exploratory data science tasks, mathematical formulations, and algorithmic solution blueprints.
• Model Engineering Loop: Perform structured feature engineering, sample selection, model training, hyperparameter optimization, and statistical validation across diverse algorithm classes.
• Production Microservice Packaging: Wrap validated machine learning pipelines into secure, containerized software microservices exposed via reliable application programming interfaces.
• Governance & Tracking: Establish model monitoring configurations to track feature drift, analyze concept decay, measure latency drops, and enforce model explainability metrics.
• Exploratory Reporting: Construct high-impact interactive visualization layers and analytics reports to deliver complex statistical insights to business stakeholders.

Required Skills & Experience

• Minimum 5+ years of hands-on experience in data science, machine learning, statistical modelling, or applied AI solution development.
• Strong ability to translate business and operational challenges into clear data science problems, mathematical formulations, analytical hypotheses and algorithmic solution approaches.
• Hands-on experience with feature engineering, data preparation, sample selection, model training, hyperparameter optimisation and statistical model validation.
• Strong knowledge of machine learning algorithms, including supervised learning, unsupervised learning, classification, regression, clustering, anomaly detection and predictive modelling techniques.
• Experience building and validating machine learning pipelines using Python-based data science libraries such as Pandas, NumPy, scikit-learn, PySpark, TensorFlow, PyTorch or comparable frameworks.
• Ability to package validated machine learning models into production-ready services using APIs, containers, Docker and microservice-based deployment patterns.
• Good understanding of model governance, including model monitoring, feature drift, concept drift, latency tracking, explainability metrics and performance degradation analysis.
• Experience creating interactive dashboards, visual analytics and business-facing reports using tools such as Power BI, Tableau, Plotly, Dash or comparable visualisation platforms.
• Good understanding of secure data handling, data quality, model lifecycle management, documentation and software development lifecycle practices.
• Ability to communicate complex statistical and machine learning insights clearly to business stakeholders, technical teams and decision-makers.
• Ability to work independently as well as part of a distributed project team, collaborating with data engineers, so