Mandatory Skills: Python (3.9+), Py Spark & Spark Internals, Databricks, Statistics/ML Libraries (stats models,
scikit-learn, SciPy, Pandas, NumPy DID, Synthetic Control, A/B testing, hypothesis testing, panel data methods),
API Development, Azure Cloud Platform, Kubernetes, Docker, Py Test.
Role Overview:
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.
Responsibilities:
- Develop and maintain statistical/ML modules (DID, Synthetic Control, A/B Testing, Multi-Treatment
Effects) in Python
- Build and extend Fast API 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 Sonar Cloud, Snyk, and PyTest
- Collaborate with data scientists and product teams to translate research into production-ready modules
Requirements:
- 3+ years of production experience in Python (3.9+)
- 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, 5. 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
Good-to-Have:
- Experience with Celery/Redis for async task orchestration
- Familiarity with Polars, PyArrow, or SQL Alchemy
- Background in econometrics or experimental design
- Spark UI profiling and performance benchmarking
- CI/CD tooling (Sonar Cloud, Snyk, GitHub Actions)Tips: Provide a summary of the role, what success in the position looks like, and how this role fits into the organization overall.
Responsibilities
[Be specific when describing each of the responsibilities. Use gender-neutral, inclusive language.]
Example: Determine and develop user requirements for systems in production, to ensure maximum usability
Qualifications
[Some qualifications you may want to include are Skills, Education, Experience, or Certifications.]
Example: Excellent verbal and written communication skills
Skills: lake,spark,azure,data,api,a/b testing,cloud,ml,testing,docker
