Data Science Architect

Role & responsibilities

Mandatory: Strong experience in Machine Learning, MLOps,Computer Vision,Deep Learning, LLM/GenAI and at least one cloud platform (AWS/Azure/GCP).

Preferred candidate profile

• Design scalable, secure, and high-performance data science and machine learning architectures to support advanced analytics and AI-driven decision-making.
• Lead the architecture and implementation of end-to-end analytical solutions, from data acquisition and feature engineering to model deployment and monitoring.
• Establish standards, frameworks, and best practices for data science, machine learning, MLOps, model governance, and AI solution development.
• Collaborate with business, product, technology, and analytics stakeholders to identify opportunities and translate business challenges into data science solutions.
• Define architectures for predictive analytics, forecasting, optimization, recommendation systems, NLP, computer vision, and other advanced analytical applications.
• Oversee the development and deployment of machine learning models, ensuring scalability, reliability, explainability, and operational excellence.
• Establish model lifecycle management processes, including experimentation, versioning, validation, deployment, monitoring, and continuous improvement.
• Ensure adherence to Responsible AI principles, including fairness, transparency, explainability, privacy, security, and regulatory compliance.
• Partner with Data Engineering and Enterprise Architecture teams to design integrated data ecosystems that support AI and advanced analytics workloads.
• Evaluate and recommend emerging technologies, tools, and industry best practices to enhance organizational AI and data science capabilities.
• Mentor and provide technical leadership to Data Scientists, ML Engineers, and Analytics teams while fostering innovation and knowledge sharing.
• Strong expertise in Statistics, Machine Learning, Predictive Modeling, and Advanced Analytics.Proficiency in Python, R, SQL, and leading data science libraries and frameworks.
• Experience with Machine Learning, Deep Learning, NLP, Time Series Forecasting, and Optimization techniques.
• Strong understanding of MLOps, CI/CD, model deployment, monitoring, and model governance.Experience with cloud platforms such as Azure, AWS, or GCP.
• Knowledge of distributed computing and big data technologies such as Spark, Hadoop, or equivalent platforms.
• Expertise in data visualization, storytelling, and communicating complex analytical concepts to business stakeholders.
• Strong understanding of data governance, data quality, security, privacy, and Responsible AI principles. Excellent leadership, stakeholder management, problem-solving, and consulting skills.
• Experience architecting and delivering enterprise-scale AI, analytics, and data science solutions across multiple business domains.