Company Overview

Job Summary

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  Perform exploratory data analysis (EDA) and data wrangling on raw, messy datasets to extract actionable business insights.
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  Design, build, train, and evaluate end-to-end machine learning models including regression, classification, clustering, and ensemble-based approaches.
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  Develop and implement predictive analytics solutions using traditional machine learning techniques such as Linear and Logistic Regression, Random Forests, and XGBoost.
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  Integrate modern AI capabilities such as NLP, generative AI, or advanced predictive analytics into existing products and client solutions.
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  Handle end-to-end ML workflows, from data preparation and feature engineering to model validation and deployment support.
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  Perform basic data engineering and data cleaning tasks independently in environments with evolving infrastructure.
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  Collaborate closely with clients and internal stakeholders to understand requirements and translate them into technical solutions.
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  Professionally challenge and defend technical decisions using data, metrics, and analytical insights when dealing with demanding stakeholders.
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  Adapt quickly to changing priorities, working across multiple initiatives such as model development, SQL-based analytics, and urgent dashboards.
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  Contribute to AI solution design in startup-like or consulting environments where ambiguity and rapid iteration are expected.

Requirements

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  Bachelor’s degree in Computer Science, Software Engineering, or a related field.
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  A minimum of 5+ years of professional experience in data science, machine learning, or AI engineering roles.
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  Strong background in data science foundations including exploratory data analysis, data wrangling, and deriving business insights from complex datasets using Python, Pandas, and SQL.
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  Proven experience building, training, and evaluating traditional machine learning models from scratch, including Linear Regression, Logistic Regression, Random Forests, XGBoost, classification, and clustering techniques.
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  Demonstrated ability to bridge traditional data science with modern AI applications such as NLP, predictive analytics, or generative AI.
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  Hands-on experience delivering end-to-end machine learning solutions in production or client-facing environments.
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  Comfort working on the “dirty work” of data, including cleaning, preprocessing, and basic data engineering in the absence of mature infrastructure.
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  Experience working in consulting, freelance, or startup environments with evolving roadmaps and limited operational support.
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  Strong stakeholder management skills, with the ability to use data and insights to push back professionally and defend technical decisions.
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  High adaptability and comfort with frequent context-switching across multiple technical domains.
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  Ability to operate effectively without a fully mature data warehouse or dedicated MLOps team.
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  Excellent English communication skills.
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  Availability to work in alignment with EMEA time zones.
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  This opportunity is remote for Egyptian applicants and Hybrid for Lebanese applicants.