Job Responsibilities

Build, test data and model pipelines for machine learning applications and analytics purpose.
Perform extract, transform, load operations from multiple data sources.
Design, build, test, validate, and deploy machine learning systems based on requirements to productionize models for product development, marketing campaigns and business strategies.
Develop processes and tools to monitor and analyze data quality and model performance.
Build data platform tools for stakeholders and team members for data visualization, catalog, and observability

Pre-Requisites

A drive to learn and master new technologies and techniques.
Excellent written and verbal communication skills for coordinating across teams.
Experience in programming languages such as Python, SQL.
Knowledge of machine learning data structures and modeling, software architecture, libraries and frameworks to create data science products that accomplishes outlined goals.
Experience using orchestration tools like Airflow or Prefect, distributed computing framework like Spark or Dask.
Demonstrated experience in applying DevSecOps, DataOps and MLOps.
Developed and deployed applications running on cloud systems such as AWS, Azure or Google Cloud Platform using Infrastructure as code tools such as Terraform, containerization tools like Dockers.
Experience visualizing/presenting data for stakeholders using: PowerBI, Tableau.
Experience on working with Enterprise level data platform and product would be a plus
Has a Bachelor or Masters degree in Computer Science, Statistics, Mathematics or another quantitative field.