Data Science Manager

Requirements

• Strong background in machine learning, deep learning, and NLP, with proven experience in training and fine-tuning large-scale models (LLMs, transformers, diffusion models, etc. ).
• Hands-on expertise with Parameter-Efficient Fine-Tuning (PEFT) approaches such as LoRA, prefix tuning, adapters, and quantization-aware training.
• Proficiency in PyTorch, TensorFlow, Hugging Face ecosystem, and good to have distributed training frameworks (e. g., DeepSpeed, PyTorch Lightning, Ray).
• Basic understanding of MLOps best practices, including experiment tracking, model versioning, CI / CD for ML pipelines, and deployment in production environments.
• Experience working with large datasets, feature engineering, and data pipelines, leveraging tools such as Spark, Databricks, or cloud-native ML services (AWS Sagemaker, GCP Vertex AI or Azure ML).
• Knowledge of GPU / TPU optimization, mixed precision training, and scaling ML workloads on cloud or HPC environments.
• Applied Problem-Solving

Mandatory Skill

Leadership & Collaboration

Other

Education & Experience