AI / Machine Learning Engineering Lead

Everest Group•Warren, NJ

2d•Hybrid

About The Position

We are looking for a talented AI Solution Engineer to join our innovative team. This role involves designing and implementing advanced AI models to solve complex business problems, particularly within the insurance sector. The ideal candidate will have a strong statistical background and be proficient in machine learning and data analysis. This is a hybrid position based in Warren, NJ working 3 days onsite, 2 remote. We are not considering fully remote candidates at this time.

Requirements

Machine Learning: Proficiency in machine learning algorithms and techniques. Experience with frameworks such as TensorFlow, PyTorch, or similar.
Statistical Analysis: Strong background in statistics and probability. Ability to apply statistical methods to real-world data.
Programming: Expertise in Python and/or R for data analysis and model development. Familiarity with SQL and other database technologies.
Data Analysis: Skilled in data preprocessing, cleaning, and transformation. Experience with data visualization tools to present findings effectively.
Insurance Domain Knowledge: Experience working with insurance datasets, including claims and underwriting data. Understanding of industry-specific challenges and regulatory requirements.
A bachelor's degree in computer science, Statistics, Data Science, or a related field is required.
7–9 years of overall experience, including 3–5+ years in AI/ML development and deployment.
Prior experience in the insurance industry is highly desirable.
Strong problem-solving abilities, excellent communication skills, and the ability to work collaboratively in a team environment.
Ability to stay current with the latest AI trends and technologies and apply them to improve existing solutions.

Nice To Haves

A master’s degree or relevant certifications (e.g., in machine learning or data science) are a plus.

Responsibilities

Design and Develop AI Models: Create and implement AI models with a robust statistical foundation. Ensure models are scalable and can be integrated into existing systems.
Performance Evaluation: Develop and utilize metrics to evaluate the performance of AI models. Continuously monitor and refine models to maintain high performance and accuracy.
Data Analysis: Analyze large datasets to extract meaningful insights and patterns. Validate AI models against existing benchmarks and datasets.
Collaboration: Work closely with data scientists, software engineers, and other stakeholders to align AI solutions with business objectives. Communicate findings and recommendations to non-technical team members.