Responsibilities
1. Deploy and integrate AI/machine learning models
- Develop and operate components within the MLOps platform.
- Partner with data scientists to build and operate Feature Store.
- Collaborate with relevant units to build/acquire new data sources for AI Center, EDA to facilitate developing new usecases.
- Support the validation of machine learning models.
- Designdeployment architecture for machine learning model, leverage on premise and cloud based big data platforms to refactor and optimize code for production.
- Automate data and machine learning engineering processes.
- Monitor model quality post-deployment; support initiatives to improve model quality.
2. Conduct research and acquire new machine learning techniques
- Conduct research on modern methods for AI/machine learning and engineering.
- Proactively analyze and utilize existing/new data sources to support more impactful analyses.
3. Collaborate with business units on advanced analytics-related problems
- Working with other centers/departments in EDA as well as Business Units to understand business problems to support them in better utilizing machine learning.
- Support other centers/departments in providing prescriptive and predictive analyses when needed.
4. Training: Training other EDA team members on machine learning engineering.
Qualifications
- Bachelors degree in mathematics, Statistics, Engineering, Computer Science or other Quantitative discipline.
- Minimum 3-5 years (senior)/ 2-3 years (junior) of solid experience in data science, machine learning or big data engineering.
- Proven experience in deploying AI/machine learning model to production.
- Have knowledge and ability to work with cloud platforms.
- Experience in data exploration/interpretation, working with statistical models, forecasting, machine learning algorithms, advanced analytical techniques.
- Familiar with SQL and experience with data transformation.
- Strong proficiency in at least one programming language Python/R.
- Deep understanding of AI/machine learning, big data technologies (including Hadoop/Spark), distributed computing, software development and visualization.
