Head of Machine Learning
3 weeks ago
Key responsibilities:
This role involves leading a team of data scientists and machine learning engineers to develop and implement advanced ML models, ensuring the delivery of high-quality, data-driven insights and solutions.
● Lead the design, development, and deployment of machine learning models and algorithms to solve complex business problems.
● Oversee the end-to-end machine learning lifecycle, including data preprocessing, model training, evaluation, and deployment.
● Utilize AWS SageMaker to build, train, and deploy machine learning models.
● Leverage AWS Bedrock to develop robust and scalable generative AI solutions for various applications.
● Develop and implement generative AI solutions to enhance business processes and customer experiences
. ● Implement MLOps practices, including model monitoring, and automation of the training and deployment pipeline.
● Collaborate with cross-functional teams to integrate ML models into production environments and applications.
● Provide technical leadership and mentorship to a team of data scientists and machine learning engineers.
● Communicate complex machine learning concepts and insights to non-technical stakeholders.
● Ensure the integrity and quality of data throughout the machine learning pipeline.
● Stay current with the latest advancements in machine learning, AI, and AWS technologies to continuously improve capabilities.
● SQL analytics integrated with generative AI solutions to deliver refined, data-driven insights for informed decision-making and data integrity.
Qualifications:
● Minimum of 7 years of hands-on experience in machine learning and data science roles, with significant experience in leading and managing ML projects.
● Proven track record of successfully delivering end-to-end machine learning solutions from concept to production deployment.
● Extensive expertise in AWS services such as SageMaker, AWS Bedrock, and other AI/ML tools.
● Strong proficiency in Python and familiarity with machine learning libraries such as TensorFlow, PyTorch, and Scikit-Learn.
● Experience with MLOps practices, including model monitoring, deployment automation, and CI/CD pipelines.
● Demonstrated ability to design and implement scalable and efficient machine learning architectures in cloud environments.
● Deep understanding of data engineering, feature engineering, and data preprocessing techniques.
● Experience with AutoML, hyperparameter tuning, and model optimization techniques.
● Leadership skills with the ability to lead and mentor a team of data scientists and machine learning engineers effectively. Skills : Aws Sagemaker, Aws Bedrock, PyTorch, Tensorflow, scikit-learn, statsmodels, pandas, numpy, prophet, darts, optuna, hyperopt, MLOps, AutoML, SQL
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