Data Scientist

QATOS • Full-time • Remote (US, United States) • 2m ago

Role : Data Scientist

Location : Remote (CST)

(Contrect)

Job Description

Mandatory Skill : AI Cognitive Agency

Manage GCP platform data loads in and out of the platform or within hybrid environment

Take offline models data scientists build and turn them into a real machine learning production system

Develop and deploy scalable tools and services for our clients to handle machine learning training and inference

Design the data pipelines and engineering infrastructure to support internal clients- enterprise machine learning systems at scale

Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients' machine learning systems

Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.

Support model development, with an emphasis on auditability, versioning, and data security

Facilitate the development and deployment of proof-of-concept machine learning systems

Communication and requirements from various stake holders to build final requirements and track progress

Qualifications

Experience building end-to-end systems as a GCP Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent)

MLOps within the enterprise CI/CD process for ML models

Experience deploying ML APIs in production environments in GCP using GKE

Experience in using GCP Vertex AI for ML and BigQuery

Knowledge in Terraform and Containers technologies

Experience writing data processing jobs using GCP Dataflow and Dataproc

Experience setting up ML model monitoring and autoscaling for ML prediction jobs

Strong software engineering skills in complex, multi-language systems

Fluency in Python and comfort with Linux administration

Experience working with cloud computing and database systems and cloud based various data formats NOSQL/HDFS

Experience building custom integrations between cloud-based systems using APIs

Experience developing and maintaining ML systems built with open source tools

Experience developing with containers and Kubernetes in cloud computing environments

Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.)

Ability to translate business needs to technical requirements

Strong understanding of software testing, benchmarking, and continuous integration

Exposure to machine learning methodology and best practices

Experience in deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.