IBM

Introduction

As a Data Scientist at IBM, you will help transform our clients’ data into tangible business value by analyzing information, communicating outcomes and collaborating on product development. Work with Best in Class open source and visual tools, along with the most flexible and scalable deployment options. Whether it’s investigating patient trends or weather patterns, you will work to solve real world problems for the industries transforming how we live.

Your Role and Responsibilities

The Data Scientist role is designed for a highly analytical and technically skilled individual who excels in data-driven environments. The candidate should possess a strong background in Python programming, database management, and data science methodologies. This role primarily focuses on leveraging data to drive insights and decision-making. The core responsibilities of the role include a range of data science tasks, such as collecting and cleansing data, exploring, and visualizing insights, and applying statistical and mathematical analysis techniques. It involves developing and implementing machine learning and deep learning models, managing big data infrastructure, and executing data engineering tasks. Additionally, the role requires maintaining codebase integrity through version control and designing, creating, and supporting AI-driven products to deliver impactful AI solutions.

Responsibilities include:

• Collecting and cleansing data from diverse sources for analysis, ensuring high-quality and relevant datasets (structured and unstructured) for effective decision-making.
• Exploring and visualizing data to uncover insights and trends, using advanced tools and techniques for meaningful data interpretation.
• Applying statistical and mathematical techniques to analyze data, providing robust analytical foundations for predictive modeling and inference.
• Developing and implementing machine learning and deep learning models
• Adaptation of foundation models/LLMs to address specific business challenges.
• Expertise in ML-Ops / AI-Ops
• Managing big data infrastructure and carrying out data engineering tasks, ensuring efficient data storage, processing, and retrieval.
• Utilizing version control for maintaining codebase integrity and collaboration, fostering a collaborative and error-free development environment.
• Designing, creating, and supporting AI-driven products, focusing on delivering scalable and impactful AI solutions that meet user needs and business objectives.

Required Technical and Professional Expertise

• Minimum 6+ years of experience in IT industry sing data science skills
• High proficiency in Python programming, NLP techniques and experience using AI Framework (e.g. Hugging Face)
• Knowledge of SQL and NoSQL database management.
• Strong background in data science, statistics, mathematics, and analytical techniques.
• Expertise in machine learning and deep learning methodologies
• Working knowledge and application of foundation models in addition to Fine tuning of LLMs.
• Familiarity with big data technologies and data engineering practices.
• Experience with version control systems, particularly Git, and proficiency with GitHub for code collaboration and repository management.
• Are able to report and present results to a non-technical audience.

This role is ideal for a candidate who is not only technically proficient in data science and generative AI but also skilled in integrating their analytical work with web technologies, cloud computing, and automation. The ability to communicate effectively, manage projects efficiently, and consider the ethical implications of data usage is crucial for success in this role.

Preferred Technical and Professional Expertise

• Hands-on experience in data science for 4+/6+ (depending upon Band7/8) years with minimum of 3 years of experience in deep learning
• Web development skills, including JavaScript and React, for creating sophisticated, interactive data-driven interfaces.
• Experience with cloud computing platforms (AWS/Azure/Google/IBM) to leverage advanced cloud-based services and infrastructure.
• Excellent communication skills, crucial for effective teamwork, stakeholder engagement, and clear presentation of data insights and technical concepts.
• Project management experience with a focus on agile methodologies, ensuring efficient, adaptive, and collaborative project execution.
• Awareness and understanding of ethical considerations in data science and AI, ensuring responsible and fair use of data and AI technologies.