Data Scientist

Overview

• Experience: - Design, develop, and deploy end-to-end machine learning and deep learning models for classification, regression, prediction, and computer vision tasks using TensorFlow, PyTorch, and scikit-learn.
• Build and fine-tune transformer-based LLMs (GPT, BERT, LLaMA, etc.) for tasks such as text summarization, chatbots, sentiment analysis, semantic search, and retrieval-augmented generation (RAG) systems.
• Apply prompt engineering, chain-of-thought prompting, and synthetic data generation to enhance the performance of generative AI applications.
• Collaborate with data engineers to build scalable data pipelines using Spark, Kafka, Hadoop, and Airflow.
• Implement model training pipelines and automate hyperparameter tuning, model evaluation, and performance tracking.
• Deploy ML models in production environments using AWS, Azure, or GCP, leveraging Docker, Kubernetes, and CI/CD workflows.
• Analyze complex datasets, perform statistical analyses, and conduct hypothesis testing, time-series forecasting, and feature engineering.
• Visualize and present findings using Tableau, Power BI, or Plotly, creating narratives that influence product and business strategy.
• Stay current with trends in LLMs, GenAI, responsible AI, and contribute to thought leadership within and outside the organization.

• Skill: - Bachelor's or Master’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field.
• Proficiency in Python, with strong experience in TensorFlow, PyTorch, scikit-learn, and other ML libraries.
• Experience with large language models, prompt engineering, Agent development frameworks.
• Knowledge of vector databases and semantic search systems.
• Familiarity with LLM fine-tuning and parameter-efficient training techniques (e.g., LoRA, PEFT).
• Familiarity with big data tools like Spark, Kafka, Hadoop, and workflow orchestration tools like Airflow.
• Hands-on experience with cloud platforms (AWS, Azure, or GCP) and deployment tools (Docker, Kubernetes, CI/CD).
• Strong foundation in statistics, probability, data modeling, and machine learning theory.
• Excellent communication and storytelling skills, with the ability to explain complex concepts to non-technical stakeholders.