DATA SCIENTIST

Data ScientistHands-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.

Requirements

• 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
  

6–8 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
  

What we Expect from you?

• Knowledge in 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
What you've got?

• Opportunity to work on real production algorithms
• Exposure to high-performance Python engineering,
• Hands-on API development experience
• End-to-end model deployment exposure
• Cloud-native experience on GCP