Data Scientist

Roles & Responsibilities :

Machine Learning & Data Science:

• Develop and implement advanced statistical models, predictive analytics, and optimization frameworks.
• Research, develop, and apply ML, Deep Learning, and AI techniques on large datasets.
• Conduct exploratory data analysis (EDA) and apply feature engineering techniques.
• Optimize and tune ML models for performance, scalability, and accuracy.

Large Language Models (LLMs) & NLP:

• Fine-tune and customize LLMs (GPT, Claude, Gemini, etc.) for enterprise use cases.
• Implement Retrieval Augmented Generation (RAG) pipelines for chatbots and knowledge retrieval.
• Work with vector databases for semantic search.
• Deploy LLM-powered applications for document summarization, sentiment analysis, and automation.

AI & Data-Driven Business Insights:

• Translate complex AI/ML models into actionable business insights.
• Partner with stakeholders to define data requirements and ensure the right data is captured.
• Develop proof-of-concept solutions demonstrating AI and ML capabilities.

Deployment & MLOps:

• Deploy AI/ML models using cloud technologies (Azure, AWS, GCP).
• Work with Docker, Kubernetes, and CI/CD pipelines for scalable ML deployment.

Continuous Learning & Innovation:

• Stay updated with the latest trends in AI, LLMs, GenAI, and cloud-based ML platforms.

Qualifications & Skills

• 3 - 6 years of experience working as a data scientist

Technical Skills:

• Strong experience in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras, SciPy).
• Hands-on expertise with LLMs, NLP techniques, and vector search (LangChain, Hugging Face).
• Solid understanding of statistics, probability, and data science methodologies.
• Experience with data pipelines (ETL, Spark, Hadoop, SQL, NoSQL, Data Lakes, Databricks).
• Knowledge of Cloud AI services (Azure ML, GCP Vertex AI).
• Strong software engineering principles with experience in Jupyter Notebooks, API development, and workflow automation.
• Solid understanding of Cloud Technologies (ideally Microsoft Azure) and SQL are beneficial

Soft Skills:

• Excellent communication, presentation, and collaboration skills.
• Ability to translate AI/ML insights into business impact.
• Self-motivated with a continuous learning mindset.