A career in IBM Software means you’ll be part of a team that transforms our customer’s challenges into solutions.
Seeking new possibilities and always staying curious, we are a team dedicated to creating the world’s leading AI-powered, cloud-native software solutions for our customers. Our renowned legacy creates endless global opportunities for our IBMers, so the door is always open for those who want to grow their career.
IBM’s product and technology landscape includes Research, Software, and Infrastructure. Entering this domain positions you at the heart of IBM, where growth and innovation thrive
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.
Your role and responsibilities
The Data Scientist role requires a highly analytical individual proficient in Python programming, database management, and data science methodologies. You’ll focus on extracting insights from data, developing and implementing machine learning models, managing big data infrastructure, and supporting AI-driven product development.
Key Responsibilities:
- Data Collection and Cleansing: Collect and cleanse data from diverse sources to ensure high-quality datasets for decision-making.
- Data Exploration and Visualization: Explore and visualize data using advanced techniques to uncover insights and trends.
- Statistical Analysis: Apply statistical and mathematical techniques to provide robust analytical foundations for predictive modeling.
- Machine Learning and Deep Learning: Develop and implement machine learning and deep learning models to address business challenges.
- ML-Ops / AI-Ops: Demonstrate expertise in ML-Ops / AI-Ops practices to ensure efficient model deployment and management.
- Big Data Management: Manage big data infrastructure and execute data engineering tasks for efficient data processing.
- Version Control and Collaboration: Utilize version control systems like Git for maintaining codebase integrity and fostering collaboration.
- AI-Driven Product Development: Design, create, and support AI-driven products to deliver impactful solutions aligned with user needs and business objectives.
Required technical and professional expertise
Work with Hiring Manager to ID up to 5 bullets max.
- Industry Experience: Minimum 8+ years of experience in the IT industry.
- Technical Proficiency: Proficient in Python programming, NLP techniques, and AI Frameworks (e.g., Hugging Face).
- Database Management: Knowledge of SQL and NoSQL database management.
- Data Science Skills: Strong background in data science, statistics, mathematics, and analytical techniques.
- Machine Learning Expertise: Expertise in machine learning and deep learning methodologies, including foundation models.
- Big Data Technologies: Familiarity with big data technologies and data engineering practices.
- Version Control Systems: Experience with version control systems, particularly Git, and proficiency with GitHub for code collaboration.
Preferred technical and professional experience
Work with Hiring Manager to ID up to 3 bullets max (encouraging then to focus on required skills).
- Deep Learning Experience: Hands-on experience in data science for 4+ years with a minimum of 3 years in deep learning.
- Web Development Skills: Web development skills including HTML, CSS, JavaScript, and React for creating interactive data-driven interfaces.
- Cloud Computing Experience: Experience with cloud computing platforms (AWS/Azure/Google/IBM) for leveraging advanced cloud-based services.
- Communication Skills: Excellent communication skills for effective teamwork, stakeholder engagement, and presentation of technical concepts.
- Project Management: Project management experience with a focus on agile methodologies for efficient project execution.
- Ethical Considerations: Awareness of ethical considerations in data science and AI to ensure responsible data usage.
