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.Requirements:PySpark & Spark SQL: Nested-JSON flattening (explode, structs),`mapInPandas` / pandas UDFs, window functions for velocity and distinct-count aggregates, partitioning and performance tuningDatabricks: Unity Catalog (catalogs, schemas, grants, column masks, tags, lineage), external locations on S3, cluster / compute policies incl. GPU, Auto Loader, Delta (MERGE, time travel, VACUUM), Workflows, Repos, secret scopesAWS: S3, IAM roles / instance profiles, KMS decryption in jobs, Secrets Manager, basic cost awarenessData engineering for ML: Medallion design (Bronze / Silver / Gold), schema evolution, idempotent loads, DQ profiling, Feature Engineering in UC (feature tables)Pseudonymisation in pipelines: HMAC tokenization with per-key-type keys, normalisation (Unicode NFKD, case, token sort), phonetic keys, never logging raw values, scripted deletion (DROP / VACUUM / secret destruction)Entity resolution (implementation): Blocking / candidate generation with exact and approximate keys, edit-distance tolerance, match scores, surrogate IDs, conflict counting rather than mergingImage processing at scale: Running a GPU batch job that decrypts, crops and embeds images in memory (PyTorch / timm), PCA, LSH bucketingMixed-data clustering: Hands-on: `kmodes` (k-prototypes), `gower` + `kmedoids` with sampling, R `kamila` on Databricks, `StepMix` (latent class), bootstrap ARIFeature selection: Mutual information, correlation pruning, PCA. Can run a BPSO / GA search with`mealpy`MLOps & monitoring: M*Lflow experiments and registry (aliases),Lakehouse Monitoring* (profile and drift metrics on Delta tables), SQL alerts, PSI / JS divergenceHandover: Writes runbooks and README-level documentation. Leaves a single end-to-end Workflow that someone else can runNice to haveExperience with AssureID / identity-verification JSON outputs.PyOD and basic PyTorch training (to support A on the VAEs in week 2).Responsibilities: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 independentlyAt Intellias, where technology takes center stage, people always come before processes. We're dedicated to cultivating a tech-savvy environment that empowers individuals to unlock their true potential and achieve extraordinary results. Our customized benefits not only prioritize your well-being but also charge your professional growth, making this opportunity an ideal match for tech enthusiasts like you.

Sourced from LinkedIn. Relocantly aggregates public job postings; apply on the original site.

Senior Data Scientist