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.
- Solid 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.

📌 Senior Data Scientist (Bengaluru)
🏢 Capgemini
📍 Bengaluru