Data Scientist – Machine Learning & Generative AI

Important: This is a dual-role position combining hands-on Data Science and Machine Learning work with technical training and learner mentoring. The successful candidate will contribute to real-world technical projects while also delivering instructor-led sessions, supporting learners, and mentoring them through practical assignments and projects. Candidates should be genuinely interested and capable in both responsibilities.

1- Data Science & Machine Learning Responsibilities Analyze structured and semi-structured datasets to identify patterns, trends, and actionable insights.

Perform data cleaning, preprocessing, exploratory data analysis, and feature engineering.

Build, train, evaluate, and improve machine learning models.

Work on classification, regression, clustering, forecasting, and other applied Data Science problems.

Apply appropriate statistical techniques and model evaluation methodologies.

Use Python and SQL for data extraction, transformation, analysis, and modeling.

Develop clear, reusable analytical notebooks, scripts, and workflows.

Create meaningful data visualizations and communicate technical findings effectively.

Participate in internal and client-facing Data Science and AI projects.

Collaborate with AI, engineering, and business teams on selected technical initiatives.

Document models, experiments, datasets, assumptions, and technical outcomes.

2- Generative AI Responsibilities Apply modern Generative AI tools in practical technical and business use cases.

Understand the fundamentals of Large Language Models and their applications.

Use prompt engineering techniques effectively.

Understand the fundamentals of embeddings, vector databases, and Retrieval-Augmented Generation.

Support introductory Generative AI demonstrations, exercises, and workshops.

Experiment with LLM APIs and basic AI workflows when required.

3- AI & Data Science Instructor Responsibilities Deliver professional training sessions in Python, statistics, SQL, Data Science, Machine Learning, and introductory AI topics.

Explain complex technical concepts in a clear, practical, and structured manner.

Conduct hands-on labs, workshops, exercises, and project-based learning sessions.

Mentor learners throughout assignments, technical projects, and capstone projects.

Review code, analytical approaches, machine learning models, and project outputs.

Provide structured and constructive technical feedback.

Support learners in troubleshooting programming, analytical, and machine learning challenges.

Contribute to practical exercises, case studies, datasets, and learning materials.

Track learner progress and identify areas requiring additional support.

Help learners build strong technical portfolios and real-world Data Science projects.

Education Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Computer Engineering, Software Engineering, Information Systems, Statistics, Mathematics , or another relevant technical discipline.

Postgraduate studies or recognized professional certifications in Data Science, Machine Learning, or Artificial Intelligence are considered an advantage.

Must-Have Technical Skills Strong proficiency in Python .

Strong practical experience with Pandas, NumPy, Matplotlib, and Scikit-learn .

Strong understanding of statistics, probability, exploratory data analysis, and data preprocessing .

Good understanding of supervised and unsupervised machine learning.

Practical experience with regression, classification, clustering, feature engineering, and model evaluation.

Strong working knowledge of SQL and relational databases .

Ability to work with real-world datasets and translate business problems into analytical approaches.

Experience completing end-to-end Data Science or Machine Learning projects.

Familiarity with Git/GitHub and professional development practices.

Generative AI Knowledge The candidate is not required to be a specialized Generative AI Engineer , but should have: Practical understanding of Large Language Models.

Familiarity with prompt engineering.

Experience using modern Generative AI platforms.

Basic understanding of embeddings, vector databases, and RAG.

Basic familiarity with LLM APIs.

Exposure to LangChain, LlamaIndex, or similar frameworks is a plus.

Soft Skills & Competencies Ability to work in a fast-paced, dynamic environment

Strong presentation, explanation, and communication skills.

Excellent communication and presentation skills, with the ability to explain complex concepts to diverse audiences.

Ability to simplify complex technical concepts for learners with different backgrounds.

Comfortable speaking and presenting in front of groups.

Ability to deliver structured technical training sessions.

Problem-solving mindset and analytical thinking

Demonstrated experience teaching, mentoring, or delivering technical training in an academic or corporate setting.

Strong command of English and Arabic.

Professional attitude, organization, accountability, and attention to detail.

Preferred Experience 1–4 years of practical Data Science, Machine Learning, or related technical experience.

Previous experience as an instructor, teaching assistant, mentor, tutor, or technical trainer is highly preferred.

Strong Data Science portfolio, GitHub profile, or demonstrated technical projects.

Experience working on real business or client datasets is preferred.

Exposure to Generative AI projects is an advantage.

Epsilon AI alumni are strongly preferred (Epsiloneer)

Exceptional fresh graduates with strong technical portfolios, practical projects, and demonstrated teaching ability may also be considered.