Senior Applied Data Machine Learning Scientist

Overview

Join an amazing team that is consistently recognized for our achievements and culture, including our most recent Forbes award of being one of America's Best Midsize Employers for 2024!

 

Position Summary:

We're seeking an Sr. Applied Data & Machine Learning Scientist to develop sophisticated personalization systems through advanced modeling techniques including propensity modeling, multi-objective ranking, and user modeling. In this role, you'll build ML systems that drive intelligent recommendations for both customers and agents, leveraging complex behavioral patterns and similarities to optimize multiple business objectives simultaneously. You'll work at the intersection of advanced ML techniques and practical applications to create scalable, production-ready personalization solutions.

 

Geo-Salary Information

State specific pay scales for this role are as follows:

$118,465 to $224,994  (CA, NJ, NY, WA, HI, AK, MD, CT, RI, MA)

$107,695 to $204,540 (NV, OR, AZ, CO, WY, TX, ND, MN, MO, IL, WI, FL, GA, MI, OH, VA, PA, DE, VT, NH, ME)

$96,926 to $184,086  (UT, ID, MT, NM, SD, NE, KS, OK, IA, AR, LA, MS, AL, TN, KY, IN, SC, NC, WV)

The expected base salary for this position will vary depending on a number of factors, including relevant experience, skills and location.

Responsibilities

Essential Job Functions: 

•  Design and implement propensity models to predict customer behaviors and preferences
• Develop multi-objective ranking systems that balance multiple business KPIs and user preferences
• Build end-to-end recommendation systems using advanced ranking and optimization techniques
• Implement personalization algorithms that can optimize for multiple competing objectives
• Research and apply novel approaches to customer similarity and behavioral modeling
• Design online learning systems that can adapt to changing user preferences
• Develop evaluation frameworks for multi-objective optimization problems
• Partner with MLOps team to deploy complex ranking and propensity models
• Lead experimentation on model architectures and optimization strategies

 

Qualifications

Education: 

Minimum:

• Bachelor's degree in Computer Science, Applied Statistics, or related STEM field with Machine Learning focus

Preferred:

• Master’s degree or PhD in Computer Science, Applied Statistics, or related STEM field with Machine Learning focus

Experience:

Minimum:

• 5+ years of hands-on experience building ML systems
• 3+ years experience with propensity modeling and multi-objective optimization
• Track record of implementing ranking systems in production
• Experience with similarity-based modeling and customer segmentation
• Demonstrated success in optimizing multiple business metrics simultaneously

Preferred:

• 8+ years of applied ML experience
• Deep expertise in ranking and recommendation systems
• Experience with large-scale behavioral modeling
• Track record of novel approaches to multi-objective optimization
• History of implementing advanced similarity-based systems

Skills & Abilities

Minimum:

• Expert-level implementation of ranking and propensity models
• Strong knowledge of optimization techniques and frameworks
• Experience with embedding and similarity modeling
• Expertise in multi-objective optimization approaches
• Ability to balance multiple competing KPIs
• Strong experimentation and evaluation skills

Preferred:

• Experience with advanced ranking architectures
• Expertise in multi-task learning and optimization
• Knowledge of online learning systems
• Experience with large-scale similarity computations
• Strong background in user behavior modeling
• Experience with contextual multi-armed bandits
• Track record of improving multiple business metrics simultaneously
• Experience with counterfactual evaluation techniques
• Proven ability to optimize complex objective functions

About the Company

Why choose a career at Mercury?

At Mercury, we have been guided by our purpose to help people reduce risk and overcome unexpected events for more than 60 years. We are one team with a common goal to help others. Everyone needs insurance and we can’t imagine a world without it.

 

Our team will encourage you to grow, make time to have fun, and work together to make great things happen. We embrace the strengths and values of each team member. We believe in having diverse perspectiv