
Senior Data Scientist
4 weeks ago
Key Responsibilities :
- Design, develop, and deploy advanced machine learning and deep learning models to solve complex business problems.
- Lead projects involving natural language processing (NLP), including fine-tuning transformer-based models for tasks such as classification, summarization, and generation.
- Manage the entire ML lifecycle including data preparation, model training, evaluation, optimization, deployment, and monitoring.
- Implement MLOps practices using tools like MLflow or Kubeflow to enable reproducibility, scalability, and automation of ML pipelines.
- Collaborate with cross-functional teams including data engineers, product managers, and software developers to productionize models.
- Utilize cloud platforms (AWS, GCP, Azure) and ML services (e.g., SageMaker, Vertex AI) for scalable model training and deployment.
- Apply best practices for model optimization techniques such as quantization and pruning to enhance performance.
- Integrate ML solutions into containerized environments using Docker, Kubernetes, and CI/CD pipelines.
- Work with distributed computing frameworks (e.g., Apache Spark, Ray) for handling large datasets efficiently.
- Explore and implement vector databases like FAISS or Milvus for similarity search and retrieval-based AI systems.
- Continuously monitor model performance, implement drift detection, and conduct hyperparameter tuning for model stability and accuracy.
- Stay up to date with the latest advancements in AI/ML and proactively introduce improvements into the data science stack.
Required Skill Set :
- Strong expertise in Machine Learning, Deep Learning, and MLOps
Proficiency in :
- Python, TensorFlow, PyTorch, Scikit-learn
- Hugging Face and other NLP model libraries
In-depth knowledge and hands-on experience in :
- NLP and fine-tuning transformer models
- Model lifecycle management and ML system design
- Cloud platforms : AWS (SageMaker), GCP (Vertex AI), Azure ML
- Containerization using Docker and Kubernetes
- CI/CD pipelines for ML workflows
- Distributed computing frameworks : Spark, Ray
- Vector databases : FAISS, Milvus
- Model optimization : quantization, pruning
- Model evaluation, hyperparameter tuning, and drift detection
Preferred Qualifications :
- Published work in machine learning/NLP or contributions to open-source projects.
- Experience mentoring junior data scientists or leading project teams.
(ref:hirist.tech)
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