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Job Description

Description :

Data Scientist.

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.

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 to 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.


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