Data Science Architect | ML Engineer | Bangalore

Capgemini; Hyderabad, Chennai District, Bengaluru; Hybrid; INR 2,600,000 - 4,200,000; Full time

Job summary

Capgemini Bengaluru (Bangalore) is seeking a Data Science Architect to design scalable data science and ML architectures, leading end-to-end analytics solutions from data acquisition and feature engineering to model deployment and monitoring.

You will establish standards for data science, MLOps, model governance, and responsible AI, collaborating with business, product, and technology stakeholders, while mentoring Data Scientists and ML Engineers across multiple domains.

Job description

Job Title: Data Science Architect

Location: Bangalore

Sourcer: [e-mail]

Job description:

• 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.