Machine Learning Engineer / Data Scientist (Mid-Level)

DATAMAZE . AI; Dubai; On-site; AED 300,000 - 540,000; Full time

Job summary

Datamaze.ai in Dubai is seeking a machine learning engineer to design, train and deploy models across classification, regression, clustering and recommendation tasks. You will prepare data, build scalable data pipelines, select algorithms, and visualize results for stakeholders.

Ideal candidates have 3–5 years of ML experience, strong Python/R skills, and hands-on experience with ML libraries and cloud platforms. The role focuses on production-grade ML and data-driven decision making.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, ML or Statistics, or equivalent experience.
• Strong programming skills in Python or R with ML libraries.
• Proven experience building and deploying ML models in production.
• 3–5 years of ML, data science, or related field experience.

Responsibilities

• Model Development: design, train and evaluate ML models for classification, regression, clustering and recommendation.
• Data Preparation: clean, preprocess and feature engineer large datasets to boost model performance.
• Model Deployment: deploy models into production with scalable, robust pipelines.
• Algorithm Selection: research and implement state-of-the-art algorithms to improve accuracy and efficiency.
• Collaboration: work with data engineering to build data pipelines and ensure data flow.
• Visualization and Reporting: create dashboards and visuals to communicate results to stakeholders.
• Monitoring and Maintenance: track model performance, address drift and bias, and optimize models.
• Tool Development: build tools to enable rapid experimentation and iteration of ML models.
• Documentation: maintain comprehensive docs for models, experiments and processes.

Skills

Python

R

SQL

Pandas

NumPy

TensorFlow

PyTorch

Scikit-learn

Keras

Spark

Hadoop

AWS SageMaker

Google AI Platform

Azure ML

MLOps

Visualization

Statistics

Optimization

NLP

Computer Vision

Time-series

Education

Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics, or related field

Tools

Git

Docker

CI/CD

MLflow

Job description

Key Responsibilities

• Model Development: Design, train and evaluate machine learning models to solve business problems such as classification, regression, clustering and recommendation.
• Data Preparation: Work with large and complex datasets performing data cleaning, preprocessing and feature engineering to optimize model performance.
• Model Deployment: Deploy machine learning models into production environments ensuring scalability and robustness.
• Algorithm Selection: Research and implement state-of-the-art algorithms and methodologies to enhance model accuracy and efficiency.
• Collaboration: Collaborate with data engineering teams to build data pipelines and ensure efficient data flow for machine learning workflows.
• Visualization and Reporting: Create clear and actionable visualizations and reports to communicate findings and results to stakeholders.
• Monitoring and Maintenance: Monitor model performance in production, address drift or bias issues and optimize models as needed.
• Tool Development: Build tools and frameworks to enable rapid experimentation and iteration of machine learning models.
• Documentation: Maintain comprehensive documentation for models, experiments and processes.

Qualifications

• Education: Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field (or equivalent experience).
• Technical Skills: Strong programming skills in Python, R, or similar languages; experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, or Keras; proficiency in data manipulation and analysis using tools like Pandas, NumPy, and SQL; experience with big data technologies such as Spark, Hadoop, or similar; knowledge of cloud platforms and services e.g. AWS SageMaker, Google AI Platform, Azure ML; familiarity with MLOps practices and tools for CI/CD in machine learning workflows; understanding of data visualization tools like Matplotlib, Seaborn, or Tableau; strong grasp of statistical methods, probability, and optimization techniques.
• Experience: 3-5 years of experience in machine learning, data science, or a related field; proven experience building and deploying machine learning models in production; experience with natural language processing (NLP), computer vision, or time-series analysis is a plus.
• Soft Skills: Strong problem-solving and analytical thinking abilities; excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders; ability to work independently and collaboratively within a team; curiosity and eagerness to stay updated on the latest advancements in machine learning and AI.

Education: Bachelor s or Master s degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field (or equivalent experience).

Technical Skills: Strong programming skills in Python, R, or similar languages. Experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, or Keras. Proficiency in data manipulation and analysis using tools like Pandas, NumPy, and SQL. Experience with big data technologies such as Spark, Hadoop, or similar. Knowledge of cloud platforms and services (e.g., AWS SageMaker, Google AI Platform, Azure ML). Familiarity with MLOps practices and tools for CI/CD in machine learning workflows. Understanding of data visualization tools like Matplotlib, Seaborn, or Tableau. Strong grasp of statistical methods, probability, and optimization techniques.

Experience: 3 5 years of experience in machine learning, data science, or a related field. Proven experience building and deploying machine learning models in production. Experience with natural language processing (NLP), computer vision, or time-series analysis is a plus.

Soft Skills: Strong problem-solving and analytical thinking abilities. Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders. Ability to work independently and collaboratively within a team. Curiosity and eagerness to stay updated on the latest advancements in machine learning and AI.