Singapore, Central Singapore
Full - Time

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

Required Skills
  • 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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