Senior Data Scientist

As a Senior Data Scientist at Shopmonkey, you will be a part of a globally distributed team working closely with your product and engineering counterparts. We are looking for a generalist who thrives across the full project lifecycle — from statistical modeling and data analysis to experimentation and predictive AI — all in service of real-world auto shop needs. Shopmonkey has the structured data and workflows to support serious data science work, and you'll have the rare opportunity to help build the analytical and ML foundation from the ground up, shaping how data-driven decision making scales alongside a maturing platform and defining what the future of automotive care looks like. For Bay area candidates, this role would be hybrid with 2-3 days/week in office at our Morgan Hill, CA location for collaboration. What will do: 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. We are looking for people who have: 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. Have Some of These: Deep expertise in statistical inference and hypothesis testing. Strong working knowledge of the ML and data science library ecosystem; able to evaluate and select appropriate tools and models for a given problem Experience with data visualization tools and communicating results clearly to both technical and non-technical audiences. Familiarity with LLMs and NLP frameworks (e.g., Hugging Face, LangChain) and an interest in applying them to real-world problems. Working knowledge of MLOps principles—enough to deploy, version, and monitor models in a modern cloud environment without heavy infrastructure ownership. Cloud infrastructure familiarity (e.g., GCP, AWS) and comfort working within existing data platforms. Bonus Points: Prior experience working at a high-growth startup. Experience in vertical SaaS or the automotive indust ... (truncated, view full listing at source)