Intern Machine Learning
5 days ago
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
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.
Machine Operations,MachineLearning,Machining
Employment Type : Full-time
Department / Functional Area: Engineering
Experience: years
Gender: Male
Vacancy: 1
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