Job Description
Get to Know the Team
The Fulfilment Interface Data Science team predicts the estimated time of arrival (ETA) and optimises the ETA quotation that balance among the ETA accuracy, system efficiency and marketplace throughput.
Get to Know the Role
We are looking for a Senior Machine Learning Engineer to build DNN models that predict ETA for Food orders and Transport bookings accurately under uncertainty.
You will report to the Senior Data Science Manager and work onsite at our office in Petaling Jaya.
The Critical Tasks You Will Perform
- You will develop innovative Machine Learning or Deep Learning model that can predict the ETA of the food orders accurately. You will also build Optimisation models to balance the ETA accuracy and system efficiency.
- You will establish pipelines to automate the model retraining and parameter tuning and serve the model in production.
- You will build dashboards to monitor the model accuracy and service health. This will involve tracking model performance, identifying data drift issues, and implementing improvements.
- You will create technical documents outlining the methodologies and findings of your work. You will present solutions and explain complex concepts to non-technical stakeholders.
- You will collaborate with Product Analytic and Product Manager to conduct A/B experiment and data analysis.
Job Requirements
What Essential Skills You Will Need
- A Bachelor or Master Degree in a relevant field such as Computer Science, or any relating fields
- 3+ years of background in at least two of the following domains: Machine Learning, Deep Learning, Causal Inference and Operation Research.
- 4+ years of relevant working experience in tech companies or machine learning industry
- Proficient in Python, Tensorflow/Pytorch, SQL, and Spark, git.
- Experience developing data processing ETL pipelines to support data analysis, model training, and model serving tasks.
- Familiar with Version Control Systems such as git, git-flow, and understand the full software development life-cycle.
Additional Info
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