Job Description

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.



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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

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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


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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