Data Scientist
Indexed description
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.
- 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, software developers, architects, analysts and business stakeholders.
- Languages : Flawless bilingual/native-level business German communications proficiency (C1/C2 level written and spoken) is required. The candidate must present seamlessly in German during the technical panel interview (Anbieterfachgespräch).
- Swiss Compliance : Must hold EU/EFTA nationality or possess an active, valid Swiss permanent residency/work card (C Permit or unrestricted B/G Permit).
- What We Offer:**
- Opportunity to work on challenging projects and contribute to the growth of our company
- Collaborative and dynamic work environment
- Professional development and growth opportunities
- Competitive salary and benefits package
- As part of our recruitment process, candidates may be invited to complete an initial AI-powered screening with TARA, the ALLPS.AI interview agent.
- TARA helps us make the hiring process faster, fairer, and more consistent by asking structured, role-relevant questions based on the job requirements. The interview is designed to understand your experience, skills, motivation, and suitability for the role.
- Questions about your professional background and relevant experience
- Role-specific technical or functional questions
- Questions about your motivation, availability, and communication skills
- The interview can usually be completed online in 15-20 minutes at a time that is convenient for you. Your responses will be reviewed as part of the overall selection process, together with your CV, application details, and any follow-up interviews with the hiring team.
- We use AI to support the recruitment process, but final hiring decisions are made by people. Human recruiters and hiring managers remain involved in reviewing candidates and making selection decisions.
- We are committed to a transparent, respectful, and fair candidate experience. Your data will be handled confidentially and in accordance with applicable data protection requirements.
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