In this role you'll work in one of our IBM Consulting Client Innovation Centers (Delivery Centers) where we deliver deep technical and industry expertise to a wide range of public and private sector clients around the world. Our delivery centers offer our clients locally based skills and technical expertise to drive innovation and adoption of new technology.
A Data Scientist with Advanced Analytics skills is a professional who leverages deep data and analytics expertise with strong business acumen to address business challenges. They utilize data preparation analysis and predictive modeling to forecast trends and suggest optimizations for improved business outcomes. The role requires proficiency in mathematical optimization discrete-event simulation rules programming and predictive analytics. Key technologies include . This comprehensive skill set enables Data Scientists to effectively manage and analyze diverse data types and structures ensuring data-driven decision-making and business optimization. Key skills encompasses proficiency in programming languages particularly Python and the use of development environments like PyCharm VS Code and Jupyter Notebooks. It includes expertise in data manipulation using tools such as Pandas NumPy and Dask and data visualization with Matplotlib Seaborn and Plotly. Scripting abilities in shell scripting along with experience in managing databases like SQL MongoDB Cassandra PostgreSQL and MySQL are also included. Additionally the skill set features knowledge of version control systems such as Git GitHub and GitLab and continuous integration and deployment (CI/CD) tools like Docker Podman and Jenkins. Optimization skills are highlighted with tools like IBM CPLEX and Gurobi and statistical analysis capabilities are demonstrated with SPSS SAS R and Python. The professional is also familiar with machine learning statistical modeling and custom models in applications like supply chain management pricing risk assessment and fraud detection
Data processing and programming using Python; demonstrate strong experience on Python data processing cleansing and transformation. Familiary with key Python libraries especially Pandas and NumPy. Familiarization with visual libraries Matpolib and Seaborn. Knowledge of foundational metrics such as mean median and node.
Analyze data systematically to identify trends patterns anomalies and relationships with large datasets. Insight generation. Automation by writing efficient Python code to automate repetitive data extraction. Understand descriptive statistics