Capgemini is looking for a Data Scientist to drive personalization, automation, and smarter decision-making across their digital products by leveraging AI and machine learning, including LLMs, Generative AI, and Knowledge Graphs.
Requirements
- Strong expertise in Python, SQL, and modern ML frameworks (TensorFlow, PyTorch, Scikit-Learn).
- Experience with MLOps tools (MLflow, Kubeflow, Airflow) for model deployment and monitoring.
- Proficiency in cloud platforms (GCP/AWS) and scalable data engineering.
- Strong understanding of probability theory, statistics, and experimental design (A/B Testing).
- Experience with collaborative software engineering practices (Agile, DevOps).
- Experience with Knowledge Graphs and their integration into AI/ML pipelines.
- Hands-on experience in LLMs (e.g., GPT, BERT, LLaMA, Claude) and Generative AI technologies.
Responsibilities
- Develop and optimize predictive and prescriptive models to extract insights and enhance decision-making.
- Apply deep learning and neural network techniques for customer classification, segmentation, and personalization.
- Utilize MLOps to efficiently deploy, monitor, and maintain ML models in production.
- Implement and fine-tune Large Language Models (LLMs) and Generative AI solutions for automation and user engagement.
- Explore and integrate knowledge graphs to enhance data relationships and improve AI-driven recommendations.
- Work with data engineers to design and develop robust data pipelines for large-scale ETL processing using SQL and cloud-based solutions (GCP preferred).
- Write complex SQL queries for extracting, transforming, and loading (ETL) data efficiently and implement CI/CD workflows to automate model training, deployment, and monitoring.
Other
- 7+ years of experience in Data Science, Machine Learning, or related fields.
- Hybrid in Philadelphia, PA – relocation available for the right candidate.
- Collaborate in an Agile/DevOps environment, promoting a data-centric culture and clearly communicating complex methodologies and insights to technical and non-technical audiences.
- Background in Retail and Personalization Web Technologies.
- Understanding of digital ecosystems and data-driven decision-making.
