Data Scientist @IBM

Requirements

Short company description

At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. Are you ready to lead in this new era of technology and solve some of the world's most challenging problems? If so, lets talk.

Your Role And Responsibilities

An experienced and innovative Data Scientist to lead the development of data-driven solutions for fuel blending optimization.

This role involves designing and implementing models that optimize fuel blends to meet regulatory, economic, and performance criteria.

The candidate will possess a strong understanding of AI and machine learning, with hands-on experience in designing AI models.

The candidate will also be skilled in selecting or developing suitable AI models tailored to specific problem areas.

The role will involve working closely with domain experts, engineers, and product teams to define and deliver scalable solutions that drive efficiency and sustainability in fuel operations.

Tasks

• Analysis and modelling of fuel blending processes using machine learning, and optimization techniques.
• Develop non-linear algorithms to recommend optimal non blend compositions based on cost, availability, emissions, and performance constraints.
• Select, train, and evaluate appropriate AI and machine learning models to support predictive and prescriptive analytics in fuel blending.
• Design and implement simulation tools to evaluate blending strategies under different scenarios.
  
  

Preferred Education

Master's Degree

Required Technical And Professional Expertise

Proficiency related to data analysis, modeling, and communication using the Python ecosyste

Proven experience in Retrieval-Augmented Generation (RAG) system development.

Proven experience with Agentic AI frameworks, such as AutoGen.

Expertise in libraries like Pandas, Numpy, Scikit-learn and TensorFlow (or similar frameworks).

Solid understanding of Large Language Models (LLMs) and their applications in Generative AI; demonstrated expertise in Natural Language Processing (NLP) and related technologies.