Senior Data Science

Principal Accountabilities (Add Rows as needed) Advanced Data Science & AI Implementation  Develop and apply cutting-edge machine learning techniques such as ANN, XGBoost, and deep learning models to solve complex business problems.  Ensure the use of state-of-the-art AI frameworks and methodologies to enhance model accuracy, efficiency, and scalability.  Optimize AI models by fine-tuning hyperparameters, feature engineering, and advanced algorithm selection to maximize performance. End-to-End Solution Development  Oversee the full AI project lifecycle, from data preparation and model development to deployment and monitoring.  Ensure smooth integration of AI solutions into production environments, collaborating with engineering teams for scalable deployments.  Use project management tools like JIRA to track tasks, deadlines, and progress, ensuring structured execution of AI initiatives.  Maintain a structured framework for model versioning, documentation, and reproducibility to ensure long-term sustainability. Experimentation & Continuous Improvement  Implement a systematic experimentation approach to identify gaps in current AI models and refine them iteratively.  Conduct A/B testing, error analysis, and post-deployment monitoring to assess model effectiveness and business impact.  Drive an innovation-first mindset, experimenting with new algorithms and methodologies to improve existing solutions.  Establish a feedback loop between model performance and business outcomes, ensuring AI-driven insights remain relevant and impactful. Technical Leadership & Best Practices  Enforce best practices in coding, model documentation, and version control to enhance standardization across AI initiatives.  Ensure teams stay updated with emerging AI/ML methodologies and provide structured guidance for integrating new techniques.  Act as a mentor to junior data scientists, providing hands-on guidance while establishing technical excellence within the team. Cross-Team Collaboration & Stakeholder Management  Ensure seamless integration of AI solutions into business workflows.  Lead discussions with stakeholders to align AI initiatives with operational priorities and measurable outcomes.  Standardize communication channels to ensure clarity, accountability, and alignment across teams. Scalable AI Solutions & Reusability  Drive the standardization of AI solutions to enable their scalability and adaptability across multiple business functions.  Develop reusable AI frameworks, ensuring consistency and reducing redundancy in data science efforts.  Establish best practices for documenting and sharing AI learnings across teams, improving operational efficiency and knowledge transfer.