Data Scientist-Artificial Intelligence

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You'll 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, you'll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You'll 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. As a Data Scientist specializing in Artificial Intelligence, you will help design, build, and deploy AI and machine learning solutions that solve real business problems. As an entry-level team member, you will work under the guidance of senior data scientists and AI architects across the full project lifecycle. Take part in end-to-end AI and ML projects, from problem definition and data collection through model deployment and monitoring. Work with business stakeholders and subject matter experts to understand requirements and translate them into analytical problem statements. Collect, clean, explore, and preprocess structured and unstructured data from multiple sources. Perform exploratory data analysis (EDA) to uncover patterns, trends, anomalies, and relationships in data. Build, train, evaluate, and fine-tune machine learning and deep learning models for tasks such as classification, regression, clustering, natural language processing (NLP), and computer vision. Apply statistical analysis, hypothesis testing, and feature engineering to improve model performance. Use frameworks such as TensorFlow, PyTorch, or scikit-learn to develop and experiment with models. Help integrate and deploy models into production environments using MLOps tools and cloud platforms. Build dashboards and data visualizations to communicate findings and model results to technical and non-technical audiences. Document methodologies, experiments, model performance, and assumptions in a clear and reproducible way. Follow responsible AI principles, including fairness, transparency, explainability, and data privacy. Stay current with the latest AI research, tools, and techniques, and apply relevant advances to ongoing work. Work with cross-functional teams including engineers, architects, and business analysts, and report progress to the project lead.

• Advanced Analytics Techniques: Exposure to advanced analytics techniques for structured data, including data analysis and interpretation, to inform business decisions.
• AI Frameworks and Tools: Experience working with AI frameworks such as TensorFlow, PyTorch, Keras, or Hugging Face, and tools like Github Copilot and Amazon Code Whisperer.
• Programming Skills: Proficiency in Python programming language, with experience in applying programming skills to develop and deploy AI models.
• NLP and ML Methods: Exposure to Natural Language Processing (NLP) and Machine Learning (ML) methods for unstructured content, including foundation models and large language models.
• Cloud Platforms and Databases: Experience working with cloud platforms (e.g., Kubernetes, AWS, Azure, GCP) and relational and NoSQL databases (e.g., SQL, Postgres, DB2, MongoDB). Strong foundation in mathematics, statistics, and probability, including linear algebra, calculus, hypothesis testing, and distributions. Proficiency in Python for data analysis and machine learning, including libraries such as NumPy, pandas, and Matplotlib. Working knowledge of machine learning algorithms, such as linear and logistic regression, decision trees, random forests, support vector machines, and k-means clustering. Understanding of model evaluation metrics, cross-validation, overfitting, and bias-variance trade-off. Basic knowledge of deep learning concepts, including neural networks, activation functions, backpropagation, and common architectures (CNN, RNN, Transformer). Familiarity with at least one ML framework such as scikit-learn, TensorFlow, or PyTorch. Working knowledge of SQL and experience querying relational databases. Understanding of data wrangling, feature engineering, and preprocessing techniques. Good verbal and written communication skills in English, with the ability to explain complex findings simply. Strong problem-solving, critical thinking, and analytical skills. Willingness to work in a team, learn quickly, and take feedback. Saudi Arabia Data & Analytics Hybrid Entry Level Al Baha, SA Al Baha (1036) IBM Middle East FZ-LLC Saudi Arabia Branch Amazon Web Services Analytical Skills Artificial Intelligence Calculus Critical Thinking Data Analysis Databases Deep Learning English IBM Database 2 Inform Logistic Regression Machine Learning Mathematics MongoDB Natural Language Processing Probability Theory Computer Programming Structured Query Language Statistics Testing Transformer Architecture Azure Google Cloud Platform Not Only Structured Query Language Written communication Kubernetes Decision Trees Keras Library Neural Networks NumPy Pandas Library PyTorch Relational Databases Scikit-learn Structured Data Tensorflow Python Programming Advanced Analytics Military Intelligence Feature Engineering Hugging Face Linear Algebra Matplotlib Support Vector Machines Cross-validation Language Models Cloud Platforms K-means Clustering GitHub Copilot Backpropagation Python for Data Analysis Machine Learning Algorithms Recurrent Neural Networks Data Wrangling