As a Senior Manager in Data Science and AI, your role is pivotal in spearheading advanced analytics and AI ML initiatives, leveraging cutting-edge technologies like Generative AI and Agentic AI. Your responsibilities encompass overseeing data science teams, fostering innovation in machine learning model development, and implementing AI solutions that drive tangible business value.

Key Responsibilities:

• Lead, mentor, and expand a team consisting of data scientists, ML engineers, and AI researchers focusing on predictive analytics, generative AI models, and agentic AI systems.
• Define the AI and ML strategy in alignment with business objectives and emerging technology trends, encompassing GenAI and agentic AI capabilities.
• Supervise the entire ML lifecycle, from problem formulation and data acquisition to feature engineering, model development, validation, deployment, and monitoring.
• Drive the research and advancement of sophisticated AI models, including generative models like GPT, diffusion models, reinforcement learning agents, and autonomous AI systems.
• Collaborate with product, engineering, and data engineering teams to seamlessly integrate AI and ML solutions into scalable production environments.
• Implement best practices for MLOps, model governance, explainability, and ethical AI.
• Assess and incorporate state-of-the-art AI technologies, frameworks, and tools such as TensorFlow, PyTorch, Hugging Face, and LangChain.
• Advocate for the utilization of generative AI across various use cases like content generation, code synthesis, conversational agents, and decision automation.
• Manage cross-functional projects and collaborate with stakeholders to prioritize AI initiatives based on their business impact.
• Stay updated on industry research and actively engage with the AI community through publications or conferences as appropriate.

Qualifications - Technical Expertise:

• Strong background in Data Science, Machine Learning, and Artificial Intelligence, with hands-on experience in developing and deploying models in production.
• In-depth understanding of Generative AI technologies, with practical experience working on models such as GPT, BERT, DALLE, diffusion models, or other generative architectures.
• Familiarity with Agentic AI concepts, including reinforcement learning, autonomous decision-making systems, and intelligent agents.
• Proficiency in ML frameworks and libraries like TensorFlow, PyTorch, Scikit-learn, Hugging Face Transformers, LangChain, etc.
• Proficient in Python programming and experienced with related ML/AI libraries.
• Experience with MLOps practices and tools for CI/CD of machine learning models, such as Kubeflow, MLflow, TFX.
• Solid grasp of data engineering fundamentals and effective collaboration with data engineering teams.
• Knowledge of cloud platforms like Azure ML, AWS SageMaker, Google Vertex AI, Oracle AI services for model training and deployment.
• Understanding of AI ethics, fairness, bias mitigation, privacy, and explainability.

Leadership and Management:

• Proven track record in managing data science, ML, or AI teams.
• Ability to translate complex AI technologies into tangible business value and actionable insights.
• Strong communication and stakeholder management skills.
• Experience in agile delivery environments and managing cross-functional resources.
• Demonstrated capability in mentoring and developing technical talent. As a Senior Manager in Data Science and AI, your role is pivotal in spearheading advanced analytics and AI ML initiatives, leveraging cutting-edge technologies like Generative AI and Agentic AI. Your responsibilities encompass overseeing data science teams, fostering innovation in machine learning model development, and implementing AI solutions that drive tangible business value.

Key Responsibilities:

• Lead, mentor, and expand a team consisting of data scientists, ML engineers, and AI researchers focusing on predictive analytics, generative AI models, and agentic AI systems.
• Define the AI and ML strategy in alignment with business objectives and emerging technology trends, encompassing GenAI and agentic AI capabilities.
• Supervise the entire ML lifecycle, from problem formulation and data acquisition to feature engineering, model development, validation, deployment, and monitoring.
• Drive the research and advancement of sophisticated AI models, including generative models like GPT, diffusion models, reinforcement learning agents, and autonomous AI systems.
• Collaborate with product, engineering, and data engineering teams to seamlessly integrate AI and ML solutions into scalable production environments.
• Implement best practices for MLOps, model governance, explainability, and ethical AI.
• Assess and incorporate state-of-the-art AI technologies, frameworks, and tools such as TensorFlow, PyTorch, Hugging Face, and LangChain.
• Advocate for the utilization of generative AI across various use cases like content generation,