Job Description

• Perform data exploration, data cleaning, data imputation, and feature engineering on unstructured and structured data.
• Build the infrastructure for optimal extraction, transformation, and loading (ETL) of data from a wide variety of data sources.
• Develop and maintain optimal data pipeline architecture for training statistical and machine learning models such as regression and classification.
• Develop and maintain evaluations to measure the effectiveness of training data. This includes measuring the capabilities of models on a variety of tasks and domains.
• Collaborate with data scientists and machine learning engineers to develop a comprehensive data science/machine learning solution pipeline.

Requirements

• Bachelor's degree in computer science or related fields, or equivalent software engineering experience.
• Proficiency in Python programming language
• Experience in dataset processing and feature engineering using tools such as Numpy, Pandas, and Scikit-Learn
• Visualization skills using tools such as Matplotlib, Seaborn, and Bokeh
• Understanding of deep learning frameworks such as PyTorch and TensorFlow
• Understanding of SQL and NoSQL
• Understands Hadoop / Spark / Kafka / Hive / Presto
• Proficiency in source control i.e. Git

Preferred

• Deep understanding of Object-Oriented Programming (OOP) concepts such as inheritance, delegation, and abstract class
• Understanding of cloud-native technologies such as AWS, GCP, and Azure
• Experience in using Docker
• Experience in using AWS services such as S3, EC2, Glue, Sagemaker
• Experience in AWS Step Function and/or AWS Lambda is even better
• Proficiency in Scala and Java programming languages
• Enjoy iterating quickly with research prototypes and learning new technologies