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Intern Machine Learning

4 weeks ago

Dubai, Dubai, United Arab Emirates dubizzle Full time
Roles and responsibilities

As a Machine Learning Intern, youwill be participating in exciting projects covering the end-to-endData Science lifecycle - from raw data cleaning and explorationwith primary and third-party systems, through advancedstate-of-the-art data visualization and Machine learningdevelopment.

You will work in a moderncloud-based data warehousing environment hosting Machine Learningmodels alongside alongside a team of diverse, intense andinteresting 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 besuccessful.

In this role, youwill:

  • Query largedatasets with SQL and feed ML models
  • Performdata exploration to find patterns in the data and understand thestate and quality of the data available
  • UtilizePython code for analyzing data and building statistical models tosolve specific business problems
  • Evaluate MLmodels and fine tune model parameters considering the businessproblem behind
  • Collaborate with senior peers toDeploy ML models in production
  • Buildcustomer-facing reporting tools to provide insights and metricswhich track system performance
  • Being part andcontributing towards a strong team culture and ambition to be onthe cutting edge of big data
  • Participate in theoff-hours on call stability rotation to support live MLmodels

Requirements

  • Bachelor's degree in AI,Statistics, Math, Operations Research, Engineering, ComputerScience, or a related quantitativefield
  • Statistical modelling andmath
  • Basic knowledge of Machine learningalgorithms
  • Basic knowledge ofSQL
  • Basic knowledge of visualization tools suchas Periscope
  • Excellent verbal and writtencommunication
  • Strong problem solvingskills
Desired candidate profile

1. Data Preparation andPreprocessing

  • DataCleaning: Assist in preparing and cleaning data formachine learning models. This could include handling missingvalues, removing outliers, and converting data into appropriateformats.
  • FeatureEngineering: Help with creating new features ortransforming existing data to improve the performance of machinelearning algorithms.
  • DataExploration: Perform exploratory data analysis (EDA)to understand data distributions, identify trends, and visualizerelationships in the data.

2.Model Building andEvaluation

  • ModelImplementation: Assist with the implementation ofmachine learning models such as linear regression, decision trees,support vector machines, and neural networks, using tools likescikit-learn,TensorFlow, orPyTorch.
  • ModelTraining: Help train models using various datasets,tuning hyperparameters to optimizeperformance.
  • ModelEvaluation: Evaluate model performance usingappropriate metrics like accuracy, precision, recall, F1 score, orAUC-ROC, and assist in interpretingresults.

3. AlgorithmResearch andTesting

  • LiteratureReview: Conduct research on the latest machinelearning algorithms and approaches. You might be tasked withreviewing research papers and experiments to help implementcutting-edge methods inpractice.
  • Experimentation:Run experiments to test different machine learning algorithms,evaluate their performance, and understand how various approachesaffect outcomes.

4.Collaboration andReporting

  • TeamCollaboration: Work closely with senior datascientists, machine learning engineers, and other team members todevelop machine learning models or contribute to data-drivenprojects.
  • Documentation:Document your work, including code, findings, and explanations formodel choices and outcomes, so that results can be easilyinterpreted and reproduced byothers.
  • Presentation:Present findings to the team, often through reports or shortpresentations, to share insights or progress on ongoingprojects.

5. Tool andSoftwareUsage

  • MachineLearning Libraries: Gain experience using librariesand frameworks such as scikit-learn,TensorFlow,Keras,PyTorch, orXGBoost to implement and fine-tunemachine learning models.
  • DataManipulation: Use tools likePandas for data manipulation,NumPy for numerical computations, andMatplotlib orSeaborn for datavisualization.
  • VersionControl: Use Git andGitHub for code version control,helping ensure that your work can be tracked and shared efficientlywith team members.
KeySkills
Machine Operations,MachineLearning,Machining
Employment Type :Full-time
Department / Functional Area: Engineering
Experience: Not Mentioned years
Gender: Male
Vacancy: 1
Joining Date: Thu, 27 Feb 2025