Funktion:
Art der Beschäftigung: Vollzeit
Ort: Singapore
Land/Region: Singapore
Job Objectives:
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.
Job Description:
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
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
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