Data Scientist

Requirements:

• Professional Tenure: 8–10 years of experience as a Data Scientist or ML Engineer, with a proven track record of delivering production-grade AI solutions.
• Core Technical Stack: Mastery of Python and its scientific ecosystem (NumPy, Pandas,Scikit-learn, SciPy, Matplotlib).
• Advanced SQL & Big Data: Expert-level SQL skills with significant experience querying and manipulating petabyte-scale data in Google BigQuery.
• GCP Expertise: Deep hands-on experience with Vertex AI (AutoML, Pipelines, Model Registry, and Training) and Google Cloud Storage.
• Generative AI Mastery (Must-Have): * Practical experience building applications with LangChain or LlamaIndex.
• Proven expertise in RAG architectures, including vector embeddings and similarity search logic.
• Advanced Prompt Engineering skills for optimizing LLM outputs.
• Algorithmic Depth: Comprehensive understanding of supervised, unsupervised, and reinforcement learning algorithms, as well as the statistical foundations behind them.
• Deep Learning & Frameworks: Strong knowledge of Neural Networks and experience with TensorFlow or PyTorch for specialized deep learning tasks.
• Engineering & MLOps: Experience with MLflow or Kubeflow for experiment tracking and model deployment. Familiarity with Docker and Kubernetes for containerized ML workloads.
• API & Integration: Basic understanding of RESTful APIs and microservices to ensure models integrate seamlessly into broader software architectures.
• Software Best Practices: Proficiency in Git, unit testing for ML code, and reproducible research practices.