Artificial Intelligence Jobs - AVP, Lead Machine Learning Engineer, 1702

at Chubb
Location Jersey City, NJ
Date Posted May 8, 2021
Category Default
Job Type Full-time

Description

AVP, Lead Machine Learning Engineer

Data and Analytics function within the global Information Technologies leads the development and operationalization of Artificial Intelligence, Cognitive Computing and Machine Intelligence applications. Currently, we are looking for an experienced AI/ML engineer who is keen to involve in new initiative of deep learning projects across our business areas globally.

The ideal candidate will be passionate about artificial intelligence and machine learning as well as stay up to date with the latest developments in the field. You are a self-starter who will take ownership of your projects and deliver high-quality data-driven machine learning solutions.  You are adept at solving diverse business problems by utilizing a variety of different tools, strategies, algorithms and programming languages.

Responsibilities

Lead Machine Learning Engineer is responsible for understanding the business problems, identifying and applying right artificial intelligence/machine intelligence technologies to solve problems and involving in formulation and execution of technologies recipes for operational deployments.

  • Understanding business objectives and developing models that help to achieve them, along with metrics to track their accuracy and performance
  • Evaluating ML algorithms that could be used to solve a given problem and ranking them by their success probability
  • Collect and analyze data from diverse internal and external source systems and assess the effectiveness and accuracy of the data and data gathering techniques
  • Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
  • Verifying data quality, and/or ensuring it via data cleaning
  • Developing strategies for curating/labelling data for model training data and implementing them effectively
  • Defining model validation strategies and operationalizing them
  • Defining the preprocessing or feature engineering to be done on a given dataset
  • Defining data augmentation pipelines
  • Training models, tuning their hyperparameters, and optimizing output
  • Analyzing the errors of the model and designing strategies to overcome them
  • Deploying models to production
  • Stay current with emerging technologies and industry trends