Data Science- Tech Lead

As a Tech Lead at Carelon Global Solutions India, you will play a crucial role in the AI team, leveraging your strong analytical skills and US Healthcare background. Candidates with expertise in healthcare data analytics and a deep understanding of US healthcare systems are preferred for this position. **Key Responsibilities:** - Demonstrate proficiency in programming, especially in machine learning and data processing. - Utilize strong analytical skills to interpret intricate healthcare datasets and derive actionable insights. - Collaborate closely with AI/ML engineers, data scientists, and product teams to acquire and process data, debug issues, and enhance ML models. - Develop and maintain enterprise-grade data pipelines to support cutting-edge AI/ML models. - Handle various data types including structured, semi-structured, and textual data effectively. - Communicate and collaborate efficiently with cross-functional teams like engineering, product, and customer stakeholders. - Operate independently with minimal guidance, showcasing strong decision-making abilities. - Tackle complex problems and provide intelligence-driven solutions focusing on innovation and scalability. - Adapt quickly to evolving business needs and technical environments while understanding product requirements. **Technical Responsibilities:** - Design and implement statistical and machine learning models using frameworks like scikit-learn, TensorFlow, and PyTorch. - Create robust data preprocessing pipelines to manage missing values, outliers, feature scaling, and dimensionality reduction. - Specialize in Large Language Model (LLM) development, including fine-tuning, prompt engineering, and embedding optimization with frameworks like Hugging Face Transformers. - Develop and optimize LLM evaluation frameworks using metrics such as ROUGE, BLEU, and custom human-aligned evaluation techniques. - Apply advanced statistical methods such as hypothesis testing, confidence intervals, and experimental design to extract insights from complex datasets. - Develop NLP solutions for text classification, sentiment analysis, and topic modeling using classical and modern techniques. As a Tech Lead at Carelon Global Solutions India, you will play a crucial role in the AI team, leveraging your strong analytical skills and US Healthcare background. Candidates with expertise in healthcare data analytics and a deep understanding of US healthcare systems are preferred for this position. **Key Responsibilities:** - Demonstrate proficiency in programming, especially in machine learning and data processing. - Utilize strong analytical skills to interpret intricate healthcare datasets and derive actionable insights. - Collaborate closely with AI/ML engineers, data scientists, and product teams to acquire and process data, debug issues, and enhance ML models. - Develop and maintain enterprise-grade data pipelines to support cutting-edge AI/ML models. - Handle various data types including structured, semi-structured, and textual data effectively. - Communicate and collaborate efficiently with cross-functional teams like engineering, product, and customer stakeholders. - Operate independently with minimal guidance, showcasing strong decision-making abilities. - Tackle complex problems and provide intelligence-driven solutions focusing on innovation and scalability. - Adapt quickly to evolving business needs and technical environments while understanding product requirements. **Technical Responsibilities:** - Design and implement statistical and machine learning models using frameworks like scikit-learn, TensorFlow, and PyTorch. - Create robust data preprocessing pipelines to manage missing values, outliers, feature scaling, and dimensionality reduction. - Specialize in Large Language Model (LLM) development, including fine-tuning, prompt engineering, and embedding optimization with frameworks like Hugging Face Transformers. - Develop and optimize LLM evaluation frameworks using metrics such as ROUGE, BLEU, and custom human-aligned evaluation techniques. - Apply advanced statistical methods such as hypothesis testing, confidence intervals, and experimental design to extract insights from complex datasets. - Develop NLP solutions for text classification, sentiment analysis, and topic modeling using classical and modern techniques.