Machine Learning Engineer / Data Scientist (Mid-Level)

DATAMAZE . AI; Dubai; Hybrid; AED 220,345 - 312,155; Full time

Job summary

DATAMAZE . AI is seeking a highly motivated Machine Learning Engineer / Data Scientist to join our dynamic team in Dubai. This mid-level position involves designing, building, and deploying innovative models for real-world challenges.

Ideal candidates will have 3–5 years of experience in machine learning, a relevant degree, and proficiency in programming languages such as Python and R. Responsibilities include model development, data preparation, and monitoring performance in production environments.

Qualifications

• 3–5 years of experience in machine learning or data science.
• Proven experience in building and deploying machine learning models.
• Experience in natural language processing, computer vision, or time-series analysis is a plus.

Responsibilities

• Design, train, and evaluate machine learning models.
• Perform data cleaning, preprocessing, and feature engineering.
• Deploy machine learning models into production.
• Research and implement state-of-the-art algorithms.
• Collaborate with data engineering teams to build data pipelines.
• Create visualizations and reports to communicate findings.
• Monitor model performance and optimize as necessary.
• Build tools for rapid experimentation.
• Maintain comprehensive documentation.

Skills

Python

R

TensorFlow

PyTorch

Scikit-learn

Pandas

NumPy

SQL

Spark

Hadoop

AWS SageMaker

Google AI Platform

Azure ML

Matplotlib

Seaborn

Tableau

Education

Bachelor's or Master's degree in Computer Science or related field

Job description

About The Role

We are looking for a highly motivated Machine Learning Engineer / Data Scientist to join our dynamic team. In this mid-level role, you will leverage your expertise in data science and machine learning to design, build, and deploy innovative models and solutions that address real-world challenges.

Location: Hyderabad, India / Dubai, UAE

Job Type: Full-time

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.