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Deadline date:
£30000 - £56000 / year

Job Description

Job Description

Total Exp in years – 2 4

Responsibilities:

  • Work on end-to-end ML Lifecycle from acquiring data, data cleaning, model building and deployment of models
  • Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
  • Verifying data quality, and/or ensuring it via data cleaning
  • Experience in building Machine Learning and Deep Learning models either with predictive algorithms, Timeseries, NLP or Computer Vision and deployment of the same
  • Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
  • Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
  • Finding available datasets online that could be used for training and data augmentation pipelines
  • Defining validation strategies, defining preprocessing or feature engineering to be done on a given dataset
  • Training models and tuning their hyperparameters
  • Analyzing the errors of the model and designing strategies to overcome them
  • Deploying models to production
  • Ensure code paths are unit tested, defect free and integration tested
  • Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality.
  • Design and implement cloud solutions, build MLOps on Azure
  • Work with workflow orchestration tools like Kubeflow, Airflow, Argo or similar tools
  • Data science models testing, validation and tests automation.
  • Communicate with a team of data scientists, data engineers and architect, document the processes.

Mandatory Skills:

  • 2 4 years of experience in Data Science and 1-2 years as ML Engineer
  • Hands-on experience of 2+ years in writing object-oriented code using python
  • Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture.
  • In-depth knowledge of mathematics, statistics, and algorithms
  • Experience working with machine learning frameworks like Tensorflow, Caffe, etc.
  • Understanding of Data Structures, Data Systems and software architecture
  • Experience in using frameworks for building, deploying, and managing multi-step ML workflows based on Docker containers and Kubernetes.
  • Experience with Azure cloud services, Cosmos DB, Streaming Analytics, IoT messaging capacity, Azure functions, Azure compute environments, etc.

Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)