About our client
Our Client operates in the Cosmetics Industry, with its headquarters rooted strongly in France. It has its branches spread to more than 150 countries, providing employment to more than 85,000 people all over the world. They fall in the “50 Most Innovative Companies in the World”. Their core business is to provide tailor made beauty to their clients. They have the advantage of having Diverse and Globalized product lines.
- 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.
- 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
- 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