\\\\Introduction\\\\ A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. Youll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, youll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. Youll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences. \\\\Your role and responsibilities\\\\ Develop and deploy advanced data science algorithms to analyze service exception signals within SAP Cloud ALM, systematically identifying anomalies and their root causes. The first phase involves identifying the most effective models to detect each type of anomaly from exception patterns. This role combines statistical analysis and machine learning to uncover trends, detect anomalies, and provide actionable insights for service reliability improvement. \\\\Required technical and professional expertise\\\\ '\- Demonstrated expertise in Python with proficiency in data science libraries (NumPy, Pandas, Matplotlib/Seaborn) \- Hands-on experience with machine learning frameworks (scikit-learn, XGBoost, LightGBM, or similar) \- Proven track record of implementing production-grade ML models for: \- Regression \- Classification \- Clustering \- Strong object-oriented programming skills in Python with understanding of design patterns \- Experience with data aggregation, transformation, and manipulation at scale \- Proficiency in writing clean, maintainable, and well-tested code \- Practical experience designing and deploying cloud-based AI applications or microservices (FastAPI, Flask, or similar frameworks) \- Hands-on experience with containerization technologies (Docker) \- Working knowledge of CI/CD pipeline configuration and automation (Jenkins, GitLab CI, GitHub Actions, or similar) \- Experience with cloud application development lifecycle including unit testing and integration testing \- Familiarity with Kubeflow components and data pipeline orchestration \- Experience working with relational databases (PostgreSQL, SAP HANA, MySQL, or similar) \\\\Preferred technical and professional experience\\\\ '\- Experience with SAP Data Intelligence graph-based workflows \- Programming experience in enterprise languages (ABAP for SAP integration, Java for microservices) IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

The role requires demonstrated expertise in Python with proficiency in data science libraries such as NumPy, Pandas, and Matplotlib/Seaborn. Hands-on experience with machine learning frameworks like scikit-learn, XGBoost, or LightGBM is necessary, along with a proven track record of implementing production-grade ML models for regression, classification, and clustering. Strong object-oriented programming skills in Python and experience with data aggregation, transformation, and manipulation at scale are also essential. Practical experience designing and deploying cloud-based AI applications or microservices using frameworks like FastAPI or Flask, along with hands-on experience with containerization technologies such as Docker, is required. Familiarity with CI/CD pipeline configuration and automation, experience with the cloud application development lifecycle including testing, and working knowledge of relational databases are also key qualifications.