Sr. Data Scientist

TAG - The Aspen Group•Chicago, IL

1d•$128 - $145

About The Position

The Aspen Group (TAG) is one of the largest and most trusted retail healthcare business support organizations in the U.S., supporting 15,000 healthcare professionals and team members at more than 1,200 health and wellness offices across 47 states in four distinct categories: Dental care, urgent care, veterinary care, and medical aesthetics. Working in partnership with independent practice owners and clinicians, the team is united by a single purpose: to prove that healthcare can be better and smarter for everyone. TAG provides a comprehensive suite of centralized business support services that power the impact of five consumer-facing businesses: Aspen Dental, ClearChoice Dental Implant Centers, WellNow Urgent Care, Lovet Animal Hospitals and Chapter Aesthetic Studios. Each brand has access to a deep community of experts, tools and resources to grow their practices, and an unwavering commitment to delivering high-quality consumer healthcare experiences at scale. As a reflection of our current needs and planned growth we are very pleased to offer a new opportunity to join our dedicated team as a Senior Data Scientist. This role is responsible for transforming raw data from various sources to develop high quality machine learning and forecasting models informing the growth strategy across our brands and for TAG as a whole.

Requirements

BA or BS in Data Science, Computer Science, Mathematics, or other degree with equivalent work experience in advanced analytics or business forecasting roles. Advanced Degree is preferred.
4-6 years of experience in roles that leverage statistical modeling or machine learning required including experience leading projects
Hands-on experience leveraging statistical and machine learning packages in Python for statistical analysis and leveraging SQL to query data is required.
Experience working with funnel optimization, user segmentation, cohort analyses, time series analyses, and regression models
Experience with mentoring and guiding Jr. level Data Scientists
Excellent communication and interpersonal skills are required.
Experience managing cross-functional projects with multiple stakeholders is desirable.
Experience with version control tools (Github, etc.)
Ability to excel in fast paced environment, take direction, and handle multiple priorities

Nice To Haves

Experience with re-training, modifying, and evaluating open source LLM algorithms to be deployed in business use cases (needs additional detail). Deployment experience is a plus
Proven track record of partnering with executive-level audiences to identify opportunities, build out the framework for potential models, and deliver a recommended approach to the business
Experience using data visualization software like Tableau or PowerBI is a plus.

Responsibilities

Model Development Build, validate, and deploy predictive forecasting models to predict demand, revenue, and market trends
Design and execute A/B tests and multivariate experiments to measure the impact of pricing changes and promotional strategies
Build elasticity models and scenario simulations to forecast revenue, margin, and adoption under different pricing and promotional strategies
Apply statistical and machine learning methods to identify patterns, trends, and insights
Develop and apply LLM-based models for text categorization, semantic analysis, and interpretation of unstructured data
Understand business objectives and translate them into data-driven solutions
Conduct rigorous testing of models, ensuring reasonable results and reporting back to leadership on outcomes
Identify opportunities within the business and proactively partner with business leaders to develop models using advanced ML and AI techniques to unlock stronger, more streamlined business performance
Model Ownership Monitor and report on model performance
Develop recommendations to improve model accuracy
Translate complex analytical findings into clear, actionable insights with recommendations to improve performance
Partner with leaders in Product, IT, and Brand leadership to implement tools that leverage the models, changing data outputs into a fully functioning product
Maintain well-documented, reproducible code and workflows
Continuous Learning & Development Stay current with industry trends, tools, and best practices in data science
Proactively suggest new approaches and methodologies to enhance decision-making by leadership
Collaborate with cross-functional teams (IT, product, finance, operations) evolve data science approaches to integrate the latest business insights and trends