Data Scientist / AI & ML Manager - Financial Services Domain

You will be joining an advanced AI research and product company that specializes in developing intelligent systems combining deep reasoning, natural language understanding, and adaptive learning. The company's goal is to create technologies that seamlessly support individuals and enterprises in decision-making, creativity, and automation. Key Responsibilities: - Lead end-to-end AI/ML solutioning, including problem framing, data preparation, model development, deployment, and monitoring. - Collaborate with business and product teams to identify impactful AI/ML use cases within Financial Services (BFSI, Fintech, Payments, Wealth Management). - Develop and scale ML models and pipelines using Python, Scikit-learn, TensorFlow, or PyTorch. - Conduct feature engineering, model evaluation, and performance optimization in alignment with business KPIs. - Deploy ML solutions on cloud platforms (AWS / Azure / GCP) using MLOps frameworks and CI/CD best practices. - Work closely with data engineering teams to ensure the availability of high-quality, reliable datasets. - Translate technical insights into business-friendly narratives and present findings to CXOs and senior stakeholders. - Mentor and provide guidance to junior data scientists and ML engineers, fostering a culture of learning and innovation. - Keep abreast of emerging trends in Generative AI, LLMs, and Agentic AI, and assess their relevance to business challenges. Experience and Qualifications Required: - 47 years of overall experience with a minimum of 3 years in applied Machine Learning. - Demonstrated success in delivering AI/ML projects with quantifiable business outcomes. - Proficiency in Python and major ML libraries (Scikit-learn, TensorFlow, PyTorch). - Hands-on experience in model deployment on AWS, Azure, or GCP. - Knowledge of data pipelines, APIs, and cloud-native architectures. - Exposure to BFSI use cases like credit risk modeling, fraud detection, churn prediction, customer segmentation, or marketing analytics. - Strong foundation in statistics, probability, and linear algebra. - Excellent communication, presentation, and stakeholder management abilities. - Educational background in Engineering, Computer Science, Mathematics, Statistics, or related fields (MBA or Masters in Data Science preferred). - Passion for AI innovation, experimentation, and emerging technologies. You will be joining an advanced AI research and product company that specializes in developing intelligent systems combining deep reasoning, natural language understanding, and adaptive learning. The company's goal is to create technologies that seamlessly support individuals and enterprises in decision-making, creativity, and automation. Key Responsibilities: - Lead end-to-end AI/ML solutioning, including problem framing, data preparation, model development, deployment, and monitoring. - Collaborate with business and product teams to identify impactful AI/ML use cases within Financial Services (BFSI, Fintech, Payments, Wealth Management). - Develop and scale ML models and pipelines using Python, Scikit-learn, TensorFlow, or PyTorch. - Conduct feature engineering, model evaluation, and performance optimization in alignment with business KPIs. - Deploy ML solutions on cloud platforms (AWS / Azure / GCP) using MLOps frameworks and CI/CD best practices. - Work closely with data engineering teams to ensure the availability of high-quality, reliable datasets. - Translate technical insights into business-friendly narratives and present findings to CXOs and senior stakeholders. - Mentor and provide guidance to junior data scientists and ML engineers, fostering a culture of learning and innovation. - Keep abreast of emerging trends in Generative AI, LLMs, and Agentic AI, and assess their relevance to business challenges. Experience and Qualifications Required: - 47 years of overall experience with a minimum of 3 years in applied Machine Learning. - Demonstrated success in delivering AI/ML projects with quantifiable business outcomes. - Proficiency in Python and major ML libraries (Scikit-learn, TensorFlow, PyTorch). - Hands-on experience in model deployment on AWS, Azure, or GCP. - Knowledge of data pipelines, APIs, and cloud-native architectures. - Exposure to BFSI use cases like credit risk modeling, fraud detection, churn prediction, customer segmentation, or marketing analytics. - Strong foundation in statistics, probability, and linear algebra. - Excellent communication, presentation, and stakeholder management abilities. - Educational background in Engineering, Computer Science, Mathematics, Statistics, or related fields (MBA or Masters in Data Science preferred). - Passion for AI innovation, experimentation, and emerging technologies.