Data Scientist - Miami FL (Hybrid) - CH

Role: Data Scientist
Duration: 12 Months
Location: Miami FL (Hybrid)

• Job Description:
• For Onboard PCP Pricing Automation & Personalization project
• Enable Alpha platform to support the data pipelines and data processing required. Ensuring that data is in usable format for ML Algorithms.
• Enabling feature engineering - Identifying relevant features that can be used to train the ML model. This may involve selecting relevant variables, creating new features, and transforming existing features to better represent the underlying data.
• Model selection and training: Selecting an appropriate ML algorithm and training it on the preprocessed data. This may involve selecting from a variety of algorithms, such as regression, decision trees, or neural networks, and tuning hyperparameters to optimize model performance.
• Evaluating the performance of the ML model using appropriate metrics, using cross-validation techniques to ensure the model is performing well on unseen data.
• Deployment and monitoring of the ML Models in production environment. Setting up monitoring tools to track model performance and making updates as needed.
• MARC Infrastructure Design & Implementation: will assist revenue management in building an infrastructure for revenue management analysts to directly modify the behavior of the PRE and TAP processes. will also be responsible for data cleaning and building the data pipelines necessary to support the development of pricing automation process for CEL.
• MARC - Managing through Automation: Rates and Cabins
• AzureDevOps/MLOps: A MLOps Engineer will enhance the AzureDevOps/MLOps capabilities of MARC. Proper adherence to best practices in MLOps will ensure that MARCs pricing and inventory automation processes are being properly developed, deployed, and monitored in a secure and efficient manner to reduce operational risks and increase system scalability.
• ML Algorithm and techniques
• Feature selection & engineering, identifying, and selecting relevant features and engineering new features to improve model performance.
• Machine Learning frameworks such as TensorFlow, PyTorch etc.
• Creating and maintaining Python libraries for machine learning that provides a range of algorithms for classification, regression, clustering etc.
• Skills to write efficient and scalable code for data processing, model training & deployment.
• Selecting appropriate ML algorithms and training them on the preprocessed data to create accurate predictive models.
• Cloud computing platform experience (preferably in Azure)
• Cloud based ML Services (preferred Azure ML)
• Azure services, such as ADF, Azure Functions, Azure Cosmos DB etc.
• Azure DevOps Principles & practices, including CI/CD pipelines.
• Azure Security Management for security policies, access controls
• Azure Active Directory, network security groups
• Strong data analytics skills for data profiling to train the model

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