Total Exp in years - 2 4
- 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.
- 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.)