AI / Machine Learning Engineer

Skills

A Bachelor's degree Masters or PHD in Statistics Mathematics Computer Science or another quantitative field. 4+ years of experience in building and managing AI/ML models. Familiarity with the following Concepts/software/tools: Experience in Supervised Learning Unsupervised Learning CNN RNN Deep-Learning Image Processing and NLP. Experience in Reinforcement Learning is a plus. Experience creating and using advanced machine learning algorithms and statistics: regression scenario analysis modeling clustering decision trees neural networks etc. Experience in Python Python for Data Science Data Visualization Experience working with one or more frameworks such as TensorFlow PyTorch Keras OpenCV Experience on Cloud (AWS Azure Goole) is a Plus Experience on AWS Sage maker is a Plus Experience visualizing/presenting data for stakeholders using: PowerBI ggplot etc. is a plus Coding knowledge and experience with other languages including SQL R C C++ is a plus Experience in MLOps MLOps on AWS is a plus

Description

The AI/ML Sr. Engineer will support our customer and other business teams with insights gained from analyzing images and other customer data. The ideal candidate is adept at using large data sets to find opportunities and using models to test the effectiveness of different courses of action. S/he must have strong experience using a variety of data mining/data analysis methods, using a variety of tools, building, and implementing models, using/creating algorithms and creating/running simulations. S/he must have a proven ability to communicate actionable results with their data-based insights. S/he must be comfortable working with a wide range of functional teams. The right candidate will have a passion for discovering solutions hidden in large data sets and working with internal teams to improve business outcomes.

II. Duties and Responsibilities include the following.

Mine and analyze data from company databases to drive improvement of business strategies.
Assess the effectiveness and accuracy of new data sources and data gathering techniques.
Develop custom models and algorithms to apply to data sets.
Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.
Develop and deploy AI/ML techniques and algorithms to enable and accelerate business decision making.
Partner with in-house data strategy experts to design analytically ready datasets by stitching customer data across multiple platforms and incorporating business rules.
Periodically validate model performance and update models based on changing business strategies/objectives.
Develop processes and tools to monitor and analyze model performance and data accuracy