Data Scientist

Introduction:

The candidate is a seasoned data professional with extensive experience in data strategy, engineering, analytics, and AI. With expertise in building scalable data pipelines, developing machine learning models, and deploying AI solutions, they have successfully led teams and managed complex data projects. Their proficiency spans the entire data lifecycle, from data collection and preprocessing to advanced analytics, visualization, and model deployment. They have worked extensively with cloud platforms, big data technologies, and MLOps, ensuring efficient data processing and automation. Skilled in deep learning, NLP, and generative AI, they have fine-tuned Large Language Models (LLMs) using Reinforcement Learning from Human Feedback (RLHF) and Supervised Fine-Tuning (SFT). Additionally, they have experience in business intelligence, dashboard development, and A/B testing, providing data-driven insights for decision-making. Their work includes predictive modeling for market trends, customer behavior analysis, and recommendation systems, leveraging techniques like time-series forecasting and anomaly detection. Adept in managing cross-functional teams, they have led data departments, optimized workflows, and implemented agile methodologies to drive innovation. Their expertise in cloud computing, ETL automation, and API development further strengthens their ability to build and scale data solutions. With a strong foundation in statistical modeling and AI-driven solutions, they bring a comprehensive skill set that bridges the gap between data engineering, analytics, and machine learning to deliver impactful business outcomes.

Responsibilities:

• Led the training and fine-tuning of Large Language Models (LLMs) using Reinforcement Learning from Human Feedback (RLHF) and Supervised Fine-Tuning (SFT).
• Managed a team of over 40 developers specializing in generative AI model enhancement.
• Designed and implemented workflows to optimize AI model development efficiency and consistency.
• Developed scalable data pipelines for real-time and batch processing.
• Integrated multiple data sources using ETL/ELT tools to automate workflows.
• Created and maintained centralized data warehouses for analytical readiness.
• Built and optimized interactive dashboards for business intelligence and data visualization.
• Developed machine learning models for predictive analytics, recommendation systems, and anomaly detection.
• Built NLP models for sentiment analysis, chatbot development, and customer segmentation.
• Designed and deployed AI-powered chatbots using pre-trained transformer models.
• Implemented MLOps practices for model deployment, monitoring, and automation.
• Developed web scraping tools for data collection and market analysis.
• Led data engineering efforts, including data lake and warehouse development.
• Optimized query performance and data retrieval for faster insights.
• Conducted A/B testing and statistical analysis for business decision-making.
• Managed stakeholders and business expectations, aligning AI solutions with organizational goals.
• Provided technical mentorship, knowledge transfer, and team upskilling in AI and data science.
• Applied time-series forecasting models for demand and sales predictions.
• Implemented data quality checks, monitoring, and alerting systems for reliability.
• Managed agile data projects, overseeing roadmaps, team coordination, and execution.