Machine Learning Engineering Manager

Role: <ul><li>Design, develop, deploy, and maintain data science and machine learning solutions to meet enterprise goals.</li><li>Collaborate with product owners, data scientists &amp; analysts to identify innovative &amp; optimal machine learning solutions that leverage data to meet business goals.</li><li>Contribute to development, rollout and onboarding of data scientists and ML use-cases to enterprise wide MLOps framework.</li><li>Scale the proven ML use-cases across the SAPMENA region.</li><li>Be responsible for optimal ML costs.</li></ul> Responsibilities: <ul><li>Deep understanding of business/functional needs, problem statements and objectives/success criteria</li><li>Collaborate with internal and external stakeholders including business, data scientists, project and partners teams in translating business and functional needs into machine learning problem statements and specific deliverables</li><li>Act as the ‘Conduit’ between product owners, data scientists, data analysts and data engineers to develop best-fit end-to-end ML solutions including but not limited to algorithms, models, pipelines, training, inference, testing, performance tuning, deployments</li><li>Review MVP implementations, provide recommendations and ensure ML best practices and guidelines are followed</li><li>Act as ‘Owner’ of end-to-end machine learning systems and their scaling</li><li>Translate machine learning algorithms into production-level code with distributed training, custom containers and optimal model serving</li><li>Industrialize end-to-end MLOps life cycle management activities including model registry, pipelines, experiments, feature store, CI-CD-CT-CE with Kubeflow/TFX</li><li>Accountable for creating, monitoring drifts leveraging continuous evaluation tools and optimizing performance and overall costs</li><li>Evaluate, establish guidelines, and lead transformation with emerging technologies and practices for Data Science, ML, MLOps, Data Ops</li></ul>&nbsp;Requirements: <ul><li>Preferred education qualifications: Bachelor/ Masters degree in Computer Science OR Data Science OR Applied Mathematics OR related technical degree</li><li>Required experience: 8-10 Years</li><li>5+ years in developing and deploying enterprise-scale ML solutions</li><li>Proven track record in data analysis (EDA, profiling, sampling), data engineering (wrangling, storage, pipelines, orchestration),</li><li>Proficiency in Data Science/ML algorithms such as regression, classification, clustering, decision trees, random forest, gradient boosting, recommendation, dimensionality reduction, deep learning, and ensemble</li><li>Proven expertise in Scikit-learn, XGBoost, LightGBM, TensorFlow</li><li>Prior experience on MLOps with Kubeflow or TFX</li><li>Advanced programming skills with Python/R and SQL</li><li>Prior experience on Data Science &amp; ML Engineering in public clouds (such as Google Cloud, AWS, Azure)</li><li>Strong technical understanding of Data &amp; Analytics concepts</li><li>Google Cloud Platform certifications (Professional Machine Learning Engineer) will be a big plus</li><li>Experience in Retail/FMCG domain is preferred</li><li>Experience in training with large volume of data (&gt;100 GB)</li><li>Experience in delivering ML projects using Agile methodologies is preferred</li><li>Proven ability to effectively communicate technical concepts and results to technical &amp; business audiences in a comprehensive manner</li><li>Proven ability to work proactively and independently to address product requirements and design optimal solutions</li><li>Fluency in English, strong communication and organizational capabilities; and ability to work in a matrix/ multidisciplinary team</li></ul>

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Machine Learning Engineering Manager

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Machine Learning Engineering Manager

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