Data Scientist

Data Collection and Cleaning: Collect and pre-process large datasets, ensuring data quality and integrity. Feature Engineering: Identify and engineer relevant features for model development and performance improvement. Data Analysis: Apply statistical and machine learning techniques to analyze data, identify trends, correlations, and patterns, and develop predictive models. Hypothesis Testing: Formulate and test hypotheses to answer specific business questions and guide decision-making. Data Visualization: Create data visualizations and dashboards to present complex data in an understandable and actionable manner. Predictive Modeling: Develop and implement machine learning models to make predictions and recommendations based on data. Data Interpretation: Translate data findings into actionable insights and strategic recommendations for stakeholders. Data-driven Decision Support: Collaborate with cross-functional teams to provide data-driven insights that inform business strategies and decisions. Data Privacy and Security: Ensure that data handling and analysis comply with data privacy and security regulations. Generative AI Model Training & Fine-tuning: Train/Fine-tune generative AI models using state-of-the-art training techniques such as LoRA/QLoRA. Agentic AI & Retrieval-Augmented Generation (RAG) Design: Design, develop, and optimize Agentic AI workflows and RAG (Retrieval-Augmented Generation) models to facilitate effective information retrieval and generation. Vector Database Management & Information Retrieval: Utilize vector databases and advanced indexing techniques to efficiently store and retrieve relevant information for conversational contexts. Continual Learning: Stay up-to-date with the latest data science techniques and tools and apply them to improve processes. Communication: Effectively communicate findings and insights to both technical and non-technical stakeholders through reports and presentations.