Machine Learning Engineer

Full time at Dubizzle in UAE

Posted on February 17, 2025

Job details

Roles and responsibilities As the Machine Learning Engineer, you will be participating in exciting projects covering the end-to-end Data Science lifecycle - from raw data cleaning and exploration with primary and third-party systems, through advanced state-of-the-art data visualization and Machine learning development. You will work in a modern cloud-based data warehousing environment hosting Machine Learning models alongside a team of diverse, intense and interesting co-workers. You will liaise with other departments - such as product & tech, the core business verticals, trust & safety, finance and others - to enable them to be successful. In this role, you will:

Work on regression and classification problems on tabular, textual and image data
Work on forecasting, anomaly detection and time-series analysis
Build recommendation engines
Work on GPT-based applications using stock models for various business use cases
Query large datasets in AWS Redshift to extract the necessary data that will feed ML models
Perform data exploration to find patterns in the data and understand the state and quality of the data available
Utilize Python code for analyzing data and building statistical models to solve specific business problems
Evaluate ML models and fine tune model parameters considering the business problem behind
Collaborate with senior peers to Deploy ML models into production that work as standalone data services
Build customer-facing reporting tools to provide insights and metrics which track system performance
Participate in the off-hours on call stability rota to support live ML models
Own at least one ML product that is in production

Requirements

Master's degree in AI, Statistics, Math, Operations Research, Engineering, Computer Science, or a related quantitative field
2+ years of working experience in Machine Learning
Experience with AWS is a plus
Knowledge in Statistical modelling and maths
Intermediate knowledge of Python's ML stack: Pandas, Matplotlib, Sklearn, Tensorflow
Intermediate knowledge of machine learning algorithms such as Linear regression, Gradient boosted trees, Neural networks
Basic knowledge of SQL, and visualization tools such as Periscope with experience in handling large datasets
Basic knowledge of statistical analysis and A/B testing
Excellent verbal and written communication
Strong problem solving skills
Analytical thinking; Conceptual thinking Detail-oriented; Business Acumen
Entrepreneurial spirit and ability to think creatively; highly-driven and self-motivated; strong curiosity and strive for continuous learning

Desired candidate profile

Developing and Implementing Machine Learning Models
Model Development : Design and implement machine learning models to address specific business problems (e.g., classification, regression, clustering).
Algorithm Selection : Choose appropriate algorithms based on the problem requirements, such as supervised, unsupervised, or reinforcement learning.
Model Training and Evaluation : Train models on large datasets and evaluate their performance using metrics like accuracy, precision, recall, F1-score, or AUC (depending on the task).
Model Optimization : Tune hyperparameters, adjust algorithms, and experiment with different architectures to improve model performance.
Data Processing and Feature Engineering
Data Cleaning : Preprocess raw data, handle missing values, outliers, and ensure that the data is clean and suitable for machine learning.
Feature Engineering : Extract relevant features from raw data, transforming it into a format suitable for modeling (e.g., scaling, encoding, dimensionality reduction).
Data Integration : Combine data from different sources and ensure proper data flow into the machine learning pipeline.
Model Deployment and Integration
Deploying Models : Work with software engineers to deploy machine learning models into production environments.
API Development : Develop APIs for integrating machine learning models into larger systems or applications for real-time or batch inference.
Scalability : Ensure that machine learning models can handle large-scale data and can be efficiently used in production environments (e.g., using distributed computing, cloud services like AWS, GCP, or Azure).
Monitoring and Maintenance : Continuously monitor model performance in production, detecting issues like data drift or model degradation, and retrain models when necessary.
Collaboration with Cross-Functional Teams
Working with Data Scientists : Collaborate with data scientists to understand the problem, select the right algorithms, and develop experimental models.
Working with Software Engineers : Work with software engineers to integrate machine learning models into software products or services.
Business Stakeholders : Translate business requirements into technical solutions and communicate findings to non-technical stakeholders.

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Hiring company

Dubizzle

Dubizzle is in search of two creative people to join its expanding in-house creative agency. At the moment you
could work alone, as a team or be from Mars â€“ to us it doesnâ€™t matter as long as you can bring strong ideas
to one of the regionâ€™s leading online brands and stay relentlessly focused on the end game, to take a spark of
brilliance from brainwave to awesome execution - through spell binding story telling.

We know our brand is the glue that holds the Dubizzle universe together. The way we build our site, the way we
interact with our customers and the way we market our services all stem from the same values that are rooted
in our brand essence.

See all jobs at Dubizzle

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Machine Learning Engineer

Job details

Apply safely

Hiring company

Dubizzle

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