Job Details
Type: Full Time
Post Date: 30+ days ago
Industry: Engineering And Technology
Job Description
Roles & ResponsibilitiesDesign and implement a robust data pipeline for collecting, cleaning, and preparing relevant consumer financial data.
Conduct exploratory data analysis to identify key features and patterns associated with financial distress.
Develop and evaluate various machine learning algorithms
Optimize model performance through feature engineering
Interpret model results and translate complex statistical concepts into actionable insights
Continuously monitor and improve model performance over time.
Develop clear and concise documentation for the model development process.
Stay up-to-date on the latest advancements in consumer credit modeling and machine learning.
Desired Candidate ProfileBachelor’s degree in data science, statistics, computer science, or a related field.
Proven experience in developing and deploying machine learning models, preferably in a financial context.
Strong understanding of statistical modeling techniques (e.g., logistic regression, decision trees, random forests).
Proficiency in machine learning libraries
Experience with data wrangling tools and techniques (e.g., SQL, Python libraries).
Excellent analytical and problem-solving skills.
Design and implement a robust data pipeline for collecting, cleaning, and preparing relevant consumer financial data.
Conduct exploratory data analysis to identify key features and patterns associated with financial distress.
Develop and evaluate various machine learning algorithms
Optimize model performance through feature engineering
Interpret model results and translate complex statistical concepts into actionable insights
Continuously monitor and improve model performance over time.
Develop clear and concise documentation for the model development process.
Stay up-to-date on the latest advancements in consumer credit modeling and machine learning.
Bachelor’s degree in data science, statistics, computer science, or a related field.
Proven experience in developing and deploying machine learning models, preferably in a financial context.
Strong understanding of statistical modeling techniques (e.g., logistic regression, decision trees, random forests).
Proficiency in machine learning libraries
Experience with data wrangling tools and techniques (e.g., SQL, Python libraries).
Excellent analytical and problem-solving skills.
A UAE-based facilities management company that carries the rich legacy of its parent organization, Etisalat. The company's roots go back to 1976, when it began as an in-house department mandated by Etisalat to manage the day-to-day upkeep of its fast-expanding and diverse infrastructure.
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