Head of Machine Learning

Head of Machine Learning

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

▶️ Head of Machine Learning
🖊️ VNR Advisory
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