Lakehouse Machine Learning Engineer
ISC2 · Remote, UNAVAILABLE, US
About this role
## Overview ISC2 is building a modern lakehouse and using it to run machine learning across the membership business. This role covers the full lifecycle—from data pipelines to stakeholder-ready results. > **Note:** This position is **not available to residents of California**. ## Responsibilities - Build and maintain **Python/Spark pipelines** through **bronze, silver, and gold** layers, including semantic datasets and ML models that consume them. - Explore data before modeling; determine what it can support and build/test the resulting features. - Partner with stakeholders to decide which questions are worth answering. - Work across model types, including: - survival and time-to-event - forecasting - classification and propensity - sequence models - recommenders - causal evaluation - Deploy and monitor models in production (e.g., **experiment tracking, model registry, scheduled inference, drift/decay monitoring**). - Deliver results to business teams via **governed semantic tables** feeding dashboards and CDP systems; explain outcomes in clear, non-technical terms. - As the platform matures, take on **applied LLM work** (structured extraction from free text, retrieval over governed data). - Build within security/governance requirements (access controls, data protection, auditability, and human review when decisions affect members). - Turn successful work into reusable patterns (templates, shared feature/evaluation code, implementation standards). - Validate ideas with small proofs of concept before production build. ## Qualifications - Strong **ETL** skills (ability to assemble datasets). - Proficiency in **Python and SQL**; comfortable across enterprise source systems. - ML stack experience: **scikit-learn** (minimum) and at least one deep learning framework such as **PyTorch or TensorFlow**. - Broad ML knowledge, including: - knowing when to use survival analysis vs. churn classification - distinguishing causal vs. predictive questions - hyperparameter tuning, cross-validation, and generalization checks - careful validation, uncertainty communication - evaluation and **bias mitigation** - Understanding of **data security, privacy, and compliance**, including how constraints shape what can be built. - Ability to work within **access controls, data protection, and auditability**. - Knowledge of **Databricks** (especially **Unity Catalog, Workflows, MLflow** — a plus). - Ability to perform cohort-based or hierarchical forecasting at scale (a plus). - Working knowledge of **Salesforce** (a plus). - Relevant certifications (a plus): - Databricks Data Engineer or Machine Learning Associate/Professional - Azure AI Engineer or Data Scientist Associate ## Education & Experience - Bachelor’s or Master’s in an IT field preferred. - Consideration for candidates with a high school diploma/equivalent and **7+ years** hands-on experience in data engineering and applied ML (enterprise preferred). - Also requires **3+ years** hands-on experience in data engineering and applied ML. - Experience deploying and monitoring models in production. - Experience with MLflow (or equivalent) for tracking/registry/scheduled inference. - Production experience building in a **medallion architecture** (bronze/silver/gold) or equivalent layered model in a governed data-catalog environment. - Distributed processing with **Spark** (or comparable), open table formats such as **Delta or Iceberg**, and catalog-managed schemas/lineage/access c
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