GEDU Services
Website:
geduservices.com
Job details:
We are looking for a
Data Scientist to join our growing analytics team. You will work closely with product, engineering, and business stakeholders to turn complex data into actionable insights — building models that drive real decisions. You'll own the full lifecycle: from exploratory analysis to production deployment. You will leverage a broad set of technologies to build new features, enhance application performance, and solve complex business challenges. This position offers the opportunity to take ownership of end-to-end projects while working closely with a team of talented, likeminded professionals in a creative and analytical environment.
What We Expect From You
- Design, develop, and deploy end-to-end ML pipelines from data ingestion to model serving in production environments.
- Partner with engineering to integrate models into production pipelines and monitor their performance over time.
- Build and maintain scalable feature engineering workflows, training infrastructure, and model monitoring systems.
- Collaborate with data/ml engineers to productionize research models — translating notebooks into reliable, tested, and versioned services.
- Implement AutoML and hyperparameter optimization workflows to accelerate experimentation cycles.
- Own the full MLOps lifecycle: model versioning, A/B testing, drift detection, retraining triggers, and performance observability.
- Ensure models meet performance, fairness, and compliance standards before and after deployment.
What You Bring
- 3–5 years of experience of hands-on ML engineering in a production environment
- Core ML (Scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM)
- MLOps & Platforms (MLflow, Kubeflow, Airflow, DVCFabric)
- AI/GEN AI (OpenAI API, LangChain, RAG, Embeddings, LLM Fine-tuning)
- Python, REST APIs, FastAPI, Git, CI/CD
- Strong problem-solving and analytical skills
- Ability to work with large and complex datasets
- Experience working in Agile or collaborative development environments
- Strong communication and documentation skills
- Evaluate and integrate cloud ML platforms (Azure ML, AWS SageMaker, GCP Vertex AI, or Microsoft Fabric) into the engineering stack.
- Contribute to LLM integration, prompt engineering, and RAG pipeline development where applicable.
- Experience with AutoML frameworks (FLAML, Auto Gluon, H2O)
Click on Apply to know more.