Data Science Architect

Capgemini Engineering; Bengaluru; Hybrid; INR 4,000,000 - 8,000,000; Full time

Benefits offered by this job

250,000+ courses on learning platform

Belonging and inclusion at Capgemini

Cutting-edge projects in AI/analytics

Renewable energy-powered campuses

Job summary

Capgemini Engineering in Bengaluru is seeking a senior Data Science Architect to design scalable ML architectures for advanced analytics and AI-driven decision-making. You will lead end-to-end analytical solutions, from data acquisition to model deployment, and establish best practices across data science and MLOps.

You will mentor Data Scientists and ML Engineers, collaborate with business and technology stakeholders, and ensure governance, security, and explainability across the model

Qualifications

• 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.

Responsibilities

• Design scalable, secure, and high-performance data science and ML architectures for advanced analytics and AI-driven decision-making.
• Lead end-to-end analytical solutions from data acquisition to model deployment and monitoring.
• Establish standards, frameworks, and best practices for data science, ML, MLOps, model governance, and AI solution development.
• Collaborate with business, product, technology, and analytics stakeholders to translate business challenges into data science solutions.
• Define architectures for predictive analytics, forecasting, optimization, recommendation systems, NLP, computer vision, and other advanced analytics applications.
• Oversee development and deployment of ML models with focus on scalability, reliability, explainability, and operational excellence.
• Establish model lifecycle 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 supporting AI 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.

Skills

Statistics

Machine Learning

Predictive Modeling

Advanced Analytics

Python

R

SQL

NLP

Time Series Forecasting

Optimization

MLOps

CI/CD

Cloud Platforms

Spark

Hadoop

Data Visualization

Communication skills

Tools

Spark

Hadoop

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.

Your Role

• 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.

Your Profile

• 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.

What Will You Love Working At Capgemini

• You will have the opportunity to learn on one of the industry's largest digital learning platforms, with access to 250,000+ courses and numerous certifications.
• We’re committed to ensure that people of all backgrounds feel encouraged and have a sense of belonging at Capgemini. You are valued for who you are, and you can bring your original self to work.
• At Capgemini, you can work on cutting-edge projects in tech and engineering with industry leaders or create solutions to overcome societal and environmental challenges.
• Capgemini office campuses in India are green and run on 100% renewable electricity. We have installed Solar plants across India locations and ‘Battery Energy Storage Solution’ (BESS) in the Noida and Mumbai campuses. You will have chance to make a difference everyday.