Data Scientist

Key Responsibilities

1. Data Science & Machine Learning

• Independently execute end-to-end data science projects, including data collection, preprocessing, feature engineering, model development, and deployment
• Perform exploratory data analysis (EDA) to identify trends, patterns, and business opportunities
• Design, build, and optimize machine learning models to improve predictive accuracy and performance
• Implement model evaluation frameworks and continuously refine models based on feedback and metrics

2. GenAI & NLP Capabilities

• Work with Generative AI (GenAI) and Large Language Models (LLMs) for real-world use cases
• Apply prompt engineering techniques to optimize LLM outputs
• Leverage traditional NLP techniques (text processing, classification, entity recognition, etc.) alongside LLM-based approaches
• Understand LLM capabilities, limitations, and evaluation methodologies

3. Insight Generation & Communication

• Extract actionable insights from complex datasets and translate them into business value
• Present findings using data visualization and storytelling techniques
• Provide data-driven recommendations to support strategic decision-making

4. Collaboration & Integration

• Collaborate with data engineering teams to integrate models into scalable data pipelines
• Work with cross-functional stakeholders to translate business problems into analytical solutions
• Maintain proper documentation, versioning, and knowledge sharing

5. Tools & Technologies

• Strong proficiency in Python with libraries such as pandas, scikit-learn, matplotlib, and NLP frameworks
• Hands-on experience with Databricks or similar cloud-based platforms
• Expertise in SQL for data extraction and transformation
• Familiarity with ML lifecycle tools and model deployment practices

Preferred Qualifications

• Experience: 4–7 years in Data Science / Machine Learning roles
• Hands-on experience in ML model development and deployment
• Exposure to GenAI/LLM-based applications in production or POCs