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