Senior Data Scientist with Bayesian statistical analysis

At Intellias, where technology takes center stage, people always come before processes. By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results. That’s why we offer a range of benefits that support your well-being and charge your professional growth.
We are committed to fostering equity, diversity, and inclusion as an equal opportunity employer. All applicants will be considered for employment without discrimination based on race, color, religion, age, gender, nationality, disability, sexual orientation, gender identity or expression, veteran status, or any other characteristic protected by applicable law.
We welcome and celebrate the uniqueness of every individual. Join Intellias for a career where your perspectives and contributions are vital to our shared success.

Let’s breathe life into great tech ideas! With 3,000 people globally, Intellias is a company where benchmark technological solutions are born. Join in and take your part in digitalizing the world.

Join a transformative data and AI platform initiative aimed at modernizing enterprise-scale capabilities and enabling real-time decision-making. This project delivers a comprehensive roadmap covering AI, MLOps, data governance, and platform scalability, supporting a shift towards data-first operations and intelligent automation.

• Platform setup. With customer’s platform team, provision and configure the Databricks environment in customer’s cloud account: catalog and permissions, compute (including GPU), secrets, experiment tracking.
• Secure data ingestion. Build repeatable ingestion of raw verification outputs and document images: parsing, pseudonymisation, data-quality checks, schema handling.
• Feature engineering at scale. Implement the feature tables:
  • batch image-embedding jobs;
  • exact and approximate linkage keys;
  • cross-transaction and velocity aggregates;
  • pseudonymous entity resolution.
• Modelling support. Implement feature-selection and mixed-data clustering methods. Run model comparisons and stability tests, and support density-model training runs.
• Automation. Orchestrate the end-to-end pipeline as a scheduled, idempotent, re-runnable workflow.
• Monitoring. Set up the data-quality and drift-monitoring prototype, with alerting, on feature and score tables.
• Privacy engineering. Implement access controls, provenance fields, lineage and the scripted deletion procedure. Confirm that no raw personal data reaches the analytic layer.
• Documentation and handover. Write runbooks and give a live walkthrough, so engineers can operate everything independently