About the role

Senior Data Scientist developing data models for real-world auto shop needs at Shopmonkey. Collaborating across teams to build analytical foundations and integrating solutions into production pipelines.

Responsibilities

Design, build, and ship production-ready models across a range of problem spaces: regression, classification, clustering, ranking, and recommendation systems.
Conduct end-to-end development of data science solutions: requirements gathering, data acquisition, exploratory analysis, feature engineering, model training, evaluation, deployment, and monitoring.
Partner with stakeholders to translate ambiguous business problems into well-scoped data science projects with clear success criteria.
Define and track model performance metrics, run A/B tests, and iterate based on real-world feedback.
Perform deep exploratory data analysis to surface insights, identify data quality issues, and inform feature engineering and modeling decisions.
Work closely with ML engineers and data engineers to ensure models are integrated reliably into production pipelines and can scale appropriately.
Build and maintain analytical models and dashboards that surface actionable insights across core business areas, partnering with product and operations teams to ensure outputs drive real decisions.
Implement NLP and LLM-powered components for sentiment analysis, real-time conversation evaluation, and behavior optimization.
Translate complex analytical findings and model outputs into clear, actionable recommendations for cross-functional stakeholders.
Contribute to backlog velocity by owning appropriate tickets and delivering high-impact work in a collaborative, fast-paced environment.

Minimum of 5+ years of industry experience in applied data science; advanced degrees (Master's or PhD) may offset years of experience.
Proven experience taking models and analyses all the way to production (not just proof-of-concepts or notebooks).
Strong foundations in classical DS/ML: exploratory data analysis, prediction, classification, clustering, feature engineering, model evaluation, experimentation, etc.
Proficiency in Python; strong SQL skills for working with large-scale data.
Strong collaboration and communication skills—comfortable working with PMs, engineers, and other cross-functional team members.
A track record of working directly with business stakeholders to gather requirements, define metrics, and frame problems in data science terms.

Medical, dental, vision, and life insurance benefits available the 1st of the month following hire date
Short term and long term disability
Employee assistance program
Reimbursement for a personal health and wellness membership
Generous parental leave
401(k) available upon hire
11 paid holidays
Flexible time off - take the time off you need!
Matching donations for approved charitable organizations
Group volunteer efforts