Machine Learning Engineering Manager

Role:

• Design, develop, deploy, and maintain data science and machine learning solutions to meet enterprise goals.
• Collaborate with product owners, data scientists & analysts to identify innovative & optimal machine learning solutions that leverage data to meet business goals.
• Contribute to development, rollout and onboarding of data scientists and ML use-cases to enterprise wide MLOps framework.
• Scale the proven ML use-cases across the SAPMENA region.
• Be responsible for optimal ML costs.

Responsibilities:

• Deep understanding of business/functional needs, problem statements and objectives/success criteria
• 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
• 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
• Review MVP implementations, provide recommendations and ensure ML best practices and guidelines are followed
• Act as ‘Owner’ of end-to-end machine learning systems and their scaling
• Translate machine learning algorithms into production-level code with distributed training, custom containers and optimal model serving
• Industrialize end-to-end MLOps life cycle management activities including model registry, pipelines, experiments, feature store, CI-CD-CT-CE with Kubeflow/TFX
• Accountable for creating, monitoring drifts leveraging continuous evaluation tools and optimizing performance and overall costs
• Evaluate, establish guidelines, and lead transformation with emerging technologies and practices for Data Science, ML, MLOps, Data Ops

Requirements:

• Preferred education qualifications: Bachelor/ Masters degree in Computer Science OR Data Science OR Applied Mathematics OR related technical degree
• Required experience: 8-10 Years
• 5+ years in developing and deploying enterprise-scale ML solutions
• Proven track record in data analysis (EDA, profiling, sampling), data engineering (wrangling, storage, pipelines, orchestration),
• Proficiency in Data Science/ML algorithms such as regression, classification, clustering, decision trees, random forest, gradient boosting, recommendation, dimensionality reduction, deep learning, and ensemble
• Proven expertise in Scikit-learn, XGBoost, LightGBM, TensorFlow
• Prior experience on MLOps with Kubeflow or TFX
• Advanced programming skills with Python/R and SQL
• Prior experience on Data Science & ML Engineering in public clouds (such as Google Cloud, AWS, Azure)
• Strong technical understanding of Data & Analytics concepts
• Google Cloud Platform certifications (Professional Machine Learning Engineer) will be a big plus
• Experience in Retail/FMCG domain is preferred
• Experience in training with large volume of data (>100 GB)
• Experience in delivering ML projects using Agile methodologies is preferred
• Proven ability to effectively communicate technical concepts and results to technical & business audiences in a comprehensive manner
• Proven ability to work proactively and independently to address product requirements and design optimal solutions
• Fluency in English, strong communication and organizational capabilities; and ability to work in a matrix/ multidisciplinary team