data scientist

Intellias develops benchmark technological solutions and supports the digitalization of the world.

• Provision and configure the Databricks environment in the customer’s cloud account with the customer’s platform team, including catalogs and permissions, compute, secrets, and experiment tracking
• Build repeatable ingestion pipelines for raw verification outputs and document images, including parsing, pseudonymisation, data-quality checks, and schema handling
• Implement feature tables for batch image-embedding jobs, exact and approximate linkage keys, cross-transaction and velocity aggregates, and pseudonymous entity resolution
• Implement feature-selection and mixed-data clustering methods, run model comparisons and stability tests, and support density-model training runs
• Orchestrate the end-to-end pipeline as a scheduled, idempotent, re-runnable workflow
• Set up a data-quality and drift-monitoring prototype with alerting on feature and score tables
• Implement access controls, provenance fields, lineage, and scripted deletion procedures; confirm that raw personal data does not reach the analytic layer
• Write runbooks and README-level documentation, deliver a live walkthrough, and leave a single end-to-end Workflow that engineers can operate independently

• Experience with PySpark and Spark SQL, including nested-JSON flattening, pandas UDFs, window functions, partitioning, and performance tuning
• Experience with Databricks Unity Catalog, S3 external locations, compute policies, Auto Loader, Delta, Workflows, Repos, and secret scopes
• Experience with AWS S3, IAM roles or instance profiles, KMS decryption in jobs, and Secrets Manager; basic cost awareness
• Knowledge of ML data engineering, including Medallion design, schema evolution, idempotent loads, data-quality profiling, and feature engineering in Unity Catalog
• Experience implementing pseudonymisation in pipelines, including HMAC tokenization, normalization, phonetic keys, safe logging, and scripted deletion
• Experience implementing entity resolution, including candidate generation, approximate keys, edit-distance tolerance, match scores, surrogate IDs, and conflict counting
• Experience with GPU batch image processing using PyTorch or timm, including decryption, cropping, embeddings, PCA, and LSH bucketing
• Hands-on experience with mixed-data clustering methods, including kmodes, gower, kmedoids, kamila, StepMix, and bootstrap ARI
• Knowledge of feature selection methods, including mutual information, correlation pruning, PCA, and BPSO or GA search with mealpy
• Experience with MLOps and monitoring using MLflow, Lakehouse Monitoring, SQL alerts, PSI, and JS divergence
• Ability to write runbooks and README-level documentation
• Будет плюсом: Experience with AssureID or identity-verification JSON outputs, PyOD, basic PyTorch training, and supporting work on VAEs

• Условий в вакансии нет