We're looking for a hands-on Data Scientist with strong Python and backend skills to maintain and improve production algorithms. The focus is on performance optimization, parallel processing, bug fixing, and building/maintaining REST APIs (Django/FastAPI). ML/DL is applied pragmatically; GCP is a plus.
Key Responsibilities
- Maintain and improve production algorithms; triage and fix bugs; add regression tests
- Profile and optimize code (CPU/memory/I/O); apply parallelism/concurrency where relevant
- Design and maintain REST APIs (Django/DRF or FastAPI) for model/algorithm services
- Test for efficiency and scalability; benchmark latency/throughput; right-size costs
- Ensure high code quality: unit/integration tests, type hints, CI/CD, documentation
- Collaborate with product/data/QA to prioritize improvements and meet SLAs
Must-Have Qualifications
- 68 years in data science/software roles with production-grade Python
- Strong Python expertise (pandas, NumPy), profiling tools (cProfile, line_profiler, memory_profiler, py-spy)
- Parallel/concurrent processing (multiprocessing, threading, asyncio) and performance tuning
- REST API development with Django/DRF or FastAPI (auth, error handling, pagination)
- ML fundamentals with scikit-learn; experience deploying models to services
- Testing/quality: pytest/unit test, CI/CD, Git workflows; clear communication and documentation
Nice to Have
scikit-learn ,PyTorch or TensorFlow; experiment tracking (MLflow/W&B)
- GCP (Cloud Run/GKE, BigQuery, GCS), Docker/Kubernetes, Cloud Build/GitHub Actions
- Observability: OpenTelemetry, GCP Monitoring/Logging; Sentry
- Security practices: OAuth2/JWT, secrets management, PII handling
