Join Capgemini Engineering, a global leader in engineering services, and contribute to shaping the future of innovation. We empower world-leading companies by bringing together diverse teams of engineers, scientists, and architects.

This role offers a dynamic career path with ample opportunities for growth. You'll engage in diverse projects where no two days are the same, allowing you to make a significant impact.

As a key member, you'll drive the design of scalable, secure, and high-performance data science and machine learning architectures. Your work will be crucial for advanced analytics and AI-driven decision-making.

Lead the architecture and implementation of comprehensive analytical solutions, from data ingestion and feature engineering through model deployment and ongoing monitoring. Develop standards, frameworks, and best practices for data science, machine learning, MLOps, model governance, and AI solution development.

Collaborate with cross-functional teams, including business, product, technology, and analytics stakeholders, to identify strategic opportunities and translate complex business challenges into actionable data science solutions. Define architectures for a wide range of advanced analytical applications, including predictive analytics, forecasting, optimization, recommendation systems, NLP, and computer vision.

Oversee the development and deployment of machine learning models, ensuring they are scalable, reliable, explainable, and operationally excellent. Establish robust model lifecycle management processes, encompassing experimentation, versioning, validation, deployment, monitoring, and continuous improvement, while ensuring adherence to Responsible AI principles like fairness, transparency, and privacy.

We are seeking individuals with strong expertise in Statistics, Machine Learning, Predictive Modeling, and Advanced Analytics. Proficiency in Python, R, SQL, and leading data science libraries is essential.

Demonstrated experience with Machine Learning, Deep Learning, NLP, Time Series Forecasting, and Optimization techniques is required. A solid understanding of MLOps, CI/CD, model deployment, monitoring, and model governance is crucial. Experience with major cloud platforms such as Azure, AWS, or GCP, along with knowledge of distributed computing and big data technologies like Spark or Hadoop, is highly valued.

Exceptional data visualization, storytelling, and communication skills are necessary to convey complex analytical concepts to business stakeholders. A strong grasp of data governance, data quality, security, privacy, and Responsible AI principles is expected. Excellent leadership, stakeholder management, problem-solving, and consulting skills are key attributes for this role. Prior experience architecting and delivering enterprise-scale AI, analytics, and data science solutions across various business domains is required.