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IBM

Data Scientist-Advanced Analytics

IBM

Published 03 Apr 2026
Shenzhen, China
Full Time

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Role Highlights

Languages used

Python
SQL

Key skills

Machine Learning
Data Analysis
Integrations
CICD
Data Management
Data Visualization
Control Systems
VCS
Supply Chain Management
Statistical Analysis
Shell Scripting
Cloud
AI
Modelling
Optimization
Statistics
Deployment

Tools, Libraries and Frameworks

Jupyter Notebook
IOS
Red Hat
IBM
SAS
Pycharm
Plotly
Git
GitHub
GitLab
Docker
Jenkins
MongoDB
Cassandra
PostGres
MySQL
NoteBooks
Pandas
NumPy
Dask
Matplotlib
Seaborn

Description

\\\\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\\\\ As a Data Scientist with Advanced Analytics skills, you will leverage deep data and analytics expertise with strong business acumen to address business challenges. You will utilize data preparation, analysis, and predictive modeling to forecast trends and suggest optimizations for improved business outcomes. Your primary responsibilities will include: Develop Predictive Models: Design and implement predictive models using mathematical optimization, discrete-event simulation, and rules programming to drive business optimization. This includes utilizing tools like IBM CPLEX and Gurobi for optimization and SPSS, SAS, R, and Python for statistical analysis. Analyze Diverse Data: Manage and analyze diverse data types and structures using programming languages like Python and development environments such as PyCharm, VS Code, and Jupyter Notebooks. This involves data manipulation with Pandas, NumPy, and Dask, and data visualization with Matplotlib, Seaborn, and Plotly. Deliver Data-Driven Insights: Utilize data preparation, analysis, and predictive modeling to forecast trends and suggest optimizations for improved business outcomes. This includes applying machine learning, statistical modeling, and custom models in applications like supply chain management, pricing, risk assessment, and fraud detection. Collaborate on Solution Delivery: Work collaboratively to deliver data-driven solutions, ensuring effective data management and analysis to inform business decision-making. Maintain Technical Expertise: Stay up-to-date with industry-leading tools and technologies, including version control systems like Git, GitHub, and GitLab, and continuous integration and deployment (CI/CD) tools like Docker, Podman, and Jenkins. \\\\Required technical and professional expertise\\\\ Data Analysis and Modeling: Experience with data preparation, analysis, and predictive modeling using tools like Pandas, NumPy, Dask, Matplotlib, Seaborn, and Plotly, with the ability to forecast trends and suggest optimizations for improved business outcomes. Programming Languages: Proficiency in programming languages, particularly Python, and experience with development environments like PyCharm, VS Code, and Jupyter Notebooks. Data Management: Experience managing and analyzing diverse data types and structures, including databases like SQL, MongoDB, Cassandra, PostgreSQL, and MySQL. Optimization and Statistical Analysis: Experience with mathematical optimization tools like IBM CPLEX and Gurobi, and statistical analysis capabilities using SPSS, SAS, R, and Python. Technical Tools and Systems: Experience with version control systems like Git, GitHub, and GitLab, and continuous integration and deployment (CI/CD) tools like Docker, Podman, and Jenkins. \\\\Preferred technical and professional experience\\\\ Machine Learning Knowledge: Experience with machine learning, statistical modeling, and custom models in applications like supply chain management, pricing, risk assessment, and fraud detection. Scripting Abilities: Shell scripting abilities, along with experience in managing databases like SQL, MongoDB, Cassandra, PostgreSQL, and MySQL. Optimization Skills: Experience with tools like IBM CPLEX and Gurobi for optimization. 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.

Required Qualifications and Skills

The role requires experience with data preparation, analysis, and predictive modeling, including forecasting trends and suggesting optimizations. Proficiency in programming languages, particularly Python, is necessary, along with experience in development environments such as PyCharm, VS Code, and Jupyter Notebooks. Experience in managing and analyzing diverse data types and structures, including various databases, is also required. The position necessitates experience with mathematical optimization tools and statistical analysis capabilities. Familiarity with version control systems and continuous integration and deployment tools is expected.

Disclaimer

Disclaimer: Job and company description information and some of the data fields may have been generated via GPT-4 summarisation and could contain inaccuracies. The full external job listing link should always be relied on for authoritative information.

About the company

IBM

Size

305978

Website

ibm.com

HQ

Armonk, New York, US

Public/Private

Public Company

Description

IBM infuses core business operations with intelligence, from machine learning to generative AI, to make organizations more responsive, productive, and resilient. It helps clients put AI into action now, creating real value with trust, speed, and confidence across various areas like digital labor, IT automation, and security. The ability to utilize all data is critical, as AI's effectiveness is dependent on the quality of data fueling it, with IBM's AI, and data platform aiming to scale and accelerate AI's impact with trusted data. IBM's hybrid cloud platform offers a comprehensive approach to development, security, and operations across hybrid environments, laying a flexible foundation for leveraging data wherever it resides.

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