Data Scientist

As a Lead Data Scientist, you will play a crucial role in driving the design, development, and deployment of advanced analytics and machine learning solutions to address complex business challenges. Your deep technical expertise and hands-on delivery leadership will be instrumental in collaborating with clients and cross-functional teams to create scalable AI/ML solutions. Additionally, you will have the opportunity to mentor junior team members and contribute to enhancing the organization's AI/ML capabilities. Key Responsibilities: - AI/ML Solution Development and Delivery - Design, build, and deploy machine learning and statistical models from problem framing to productionization. - Apply advanced analytics techniques such as predictive modeling, optimization, simulation, and statistical analysis to drive business outcomes. - Utilize Generative AI approaches like LLM-based solutions, embeddings, and prompt engineering for knowledge extraction, automation, and content generation where applicable. - Collaborate with engineering teams to integrate models into production systems while ensuring scalability and performance. - Follow best practices for model versioning, monitoring, and lifecycle management, including emerging practices for LLMOps. - Technical Leadership - Translate business problems into analytical frameworks and technical solutions, including hybrid approaches combining classical ML and Generative AI. - Maintain high standards for code quality, reproducibility, and model robustness. - Lead hypothesis-driven experimentation and guide teams on feature engineering, model selection, evaluation, and prompt tuning. - Stay updated with emerging AI/ML and Generative AI techniques and evaluate their relevance to client issues. - Client Engagement - Collaborate with client stakeholders to understand business challenges and define AI/ML and data-driven use cases. - Present findings, model outputs, and recommendations in a clear and actionable manner. - Build PoCs and prototypes, including GenAI-led experiences, to demonstrate value and accelerate adoption. - Team Mentorship - Mentor junior data scientists and analysts on modeling techniques, tools, and best practices. - Conduct code reviews and provide technical feedback to ensure quality and consistency. - Contribute to internal knowledge sharing, reusable assets, and accelerators. Qualifications Required: - Education and Experience - Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field. - 8-12 years of experience in data science or AI/ML roles with strong hands-on delivery experience. - Technical Skills - Strong expertise in machine learning, statistical modeling, and optimization techniques. - Proficiency in Python (preferred) or R, and common ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn). - Working knowledge of Generative AI concepts such as LLMs, embeddings, vector databases, prompt engineering, and RAG. - Experience with data platforms, large-scale data processing (e.g., Spark), and cloud environments. - Familiarity with MLOps practices, model deployment, and monitoring frameworks. As a Lead Data Scientist, you will play a crucial role in driving the design, development, and deployment of advanced analytics and machine learning solutions to address complex business challenges. Your deep technical expertise and hands-on delivery leadership will be instrumental in collaborating with clients and cross-functional teams to create scalable AI/ML solutions. Additionally, you will have the opportunity to mentor junior team members and contribute to enhancing the organization's AI/ML capabilities. Key Responsibilities: - AI/ML Solution Development and Delivery - Design, build, and deploy machine learning and statistical models from problem framing to productionization. - Apply advanced analytics techniques such as predictive modeling, optimization, simulation, and statistical analysis to drive business outcomes. - Utilize Generative AI approaches like LLM-based solutions, embeddings, and prompt engineering for knowledge extraction, automation, and content generation where applicable. - Collaborate with engineering teams to integrate models into production systems while ensuring scalability and performance. - Follow best practices for model versioning, monitoring, and lifecycle management, including emerging practices for LLMOps. - Technical Leadership - Translate business problems into analytical frameworks and technical solutions, including hybrid approaches combining classical ML and Generative AI. - Maintain high standards for code quality, reproducibility, and model robustness. - Lead hypothesis-driven experimentation and guide teams on feature engineering, model selection, evaluation, and prompt tuning. - Stay updated with emerging AI/ML and Generative AI techniques and evaluate their