Senior Data Scientist with Bayesian statistical analysis

Location: Intellias Meet your recruiter Khrystyna Derpak khrystyna.derpak@intellias.com https://www.linkedin.com... Vacancy details AI/ML Engineering Data Scientist Senior Bulgaria, Croatia, Poland, Portugal, Spain Remote APPLY NOW REFER A FRIEND 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. What project we have for you 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. What you will do 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 What you need for this PySpark & Spark SQL: Nested-JSON flattening (explode, structs),`mapInPandas` / pandas UDFs, window functions for velocity and distinct-count aggregates, partitioning and performance tuning Databricks: 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 scopes AWS: S3, IAM roles / instance profiles, KMS decryption in jobs, Secrets Manager, basic cost awareness Data 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 merging Image processing at scale: Running a GPU batch job that decrypts, crops and embeds images in memory (PyTorch / timm), PCA, LSH bucketing Mixed-data clustering: Hands-on: `kmodes` (k-prototypes), `gower` + `kmedoids` with sampling, R `kamila` on Databricks, `StepMix` (latent class), bootstrap ARI Bayesian statistical analysis: Hands-on experience applying Bayesian methods to real-world data problems, including prior/posterior modelling, Bayesian inference, uncertainty estimation, and interpretation of probabilistic results. Feature 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 divergence Handover: Writes runbooks and README-level documentation. Leaves a single end-to-end Workflow that someone else can run Nice to have Experience with AssureID / identity-verification JSON outputs. PyOD and basic PyTorch training (to support A on the VAEs in week 2). What it’s like to work at Intellias 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. Skills AWS Python