ML Engineer/ Data science

Overview

Hybrid
Depends on Experience
Accepts corp to corp applications
Contract - W2

Skills

Machine learning
Python
AWS
GCP
data science

Job Details

An ideal candidate would be an experienced MLE. If you have a super good full stack data scientist to share, we can also consider him/her for this role.

ML Engineer:

  • Bachelor s degree or above.
  • Four or more years of work experience in software development/ML Engineering jobs.
  • At least two years of experience are in ML Engineering areas.
  • Experience with Python programming
  • Experience in cloud based development and production environments - AWS, Google Cloud Platform and On-prem clusters
  • Familiarity with large scale cloud-based platform/pipeline development and productization.
  • Have experience with basic ML practices and standard workflows.

Even better if you have one or more of the following:

  • Understand data science concepts and common needs from data scientists and data engineers.
  • Strong collaboration skills and communication skills, especially when involving (non-tech) business stakeholders.
  • Familiar with CI/CD processes and common frameworks, like Jenkins, Airflow, etc.
  • Familiar with MLOps/DevOps.

Data Scientist:

  • Independently create and/or Assist lead data scientists in developing various kinds of machine learning models for personalized customer service domain using state of the art ML algorithms such as GBM, XGBoost, Deep Learning etc. on CPU and GPU environments
  • Conduct pre-modeling activities such as data clean up, exploratory data analysis, scaling/normalization, feature engineering, etc. with data in Google Cloud Platform ecosystem, notebooks
  • Proficient with standard Data Science Libraries such as NumPy, Pandas and Deep learning. In addition, proficiency in libraries for GPUs is a huge plus
  • Knowledge of Pytorch or Tensorflow and experience with modern neural NLP approaches is a plus.
  • Developing test cases and executing test cases for developed ML models
  • Assist in performance/load testing/monitoring of the deployed models
  • Documenting the models, features and any decisions made during modeling
  • Assist ML engineers during solution deployment on on-prem and AWS for authoring/making changes to API wrappers
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