SENIOR SCIENTIST - Machine Learning

ML Engineer ? Test & Learn Platform (3+ Years Experience)

About the Role

We're looking for an ML Engineer to join our Test & Learn Platform team. You'll build and scale our experimentation and causal inference services ? from statistical engines to API integrations and cloud pipelines ? empowering business teams globally to make data-driven decisions.

What You'll Do

• Develop and maintain statistical/ML modules (DID, Synthetic Control, A/B Testing, Multi-Treatment Effects) in Python
• Build and extend FastAPI services and integrate them with our web application via SDK wrappers
• Design and optimize large-scale data pipelines using PySpark, Delta Lake, and Azure Data Lake
• Profile and resolve OOM issues in PySpark jobs ? optimize memory allocation, partitioning, broadcast joins, caching strategies, and Spark configurations
• Deploy and manage workloads on Databricks, including job clusters, notebooks, and Delta Lake tables
• Containerize and deploy services using Docker, Kubernetes, and CI/CD pipelines
• Ensure code quality and security via SonarCloud, Snyk, and pytest
• Collaborate with data scientists and product teams to translate research into production-ready modules

Must-Have Skills

• Python (3.9+) ? 3+ years of production experience
• PySpark & Spark Internals ? strong experience with Spark memory model, executor tuning, shuffle optimization, and diagnosing/resolving OOM errors (broadcast thresholds, partition skew, spill-to-disk, GC tuning)
• Databricks ? hands-on with job orchestration, cluster configuration, notebook workflows, and Delta Lake optimization (Z-ordering, compaction, caching)
• Causal Inference & Experimentation ? DID, synthetic control, A/B testing, hypothesis testing, panel data methods
• Statistics/ML Libraries ? statsmodels, scikit-learn, scipy, pandas, numpy
• API Development ? building RESTful services with FastAPI (or similar)
• Cloud (Azure) ? Azure Storage, Azure ML, Data Lake
• Docker & Kubernetes ? containerization and orchestration for ML workloads
• Testing ? writing robust unit/integration tests with pytest

Nice-to-Have

• Experience with Celery/Redis for async task orchestration
• Familiarity with Polars, PyArrow, or SQLAlchemy
• Background in econometrics or experimental design
• Spark UI profiling and performance benchmarking
• CI/CD tooling (SonarCloud, Snyk, GitHub Actions)

What Sets You Apart

• You can look at a Spark execution plan and pinpoint why a job is OOM-ing
• You think in modules ? clean separation of data processing, inference, and post-processing
• You can go from a Jupyter notebook prototype to a production-grade, testable service
• You're comfortable with both statistical rigor and software engineering best practices