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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Full Stack Data Scientist - **Company:** The Pet Circle LLC - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Sql Data Warehouse, 3d Models, Artificial Intelligence, Amazon Web Services, Data Analysis, BigQuery, Code Generation, Data Infrastructure, Python (Programming Language), SQL Databases, Alwayson, Delivery Pipeline, Large Language Models, Data Management, Machine Learning Operations, Software Version Control - **Published:** July 21, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/p61o5m46du ## About the Role * 7+ years in applied data science, statistical modelling or quantitative research, with a track record of shipping models that influenced real decisions, not just delivered analysis. * Builder mentality: you write production-quality Python or R, you care about getting things into the hands of decision-makers, and you know when a workable deployed model beats a perfect one still in development. You move fast without creating problems you cannot see. * Strong statistical modelling: regression, survival and hazard models, Bayesian inference, time series. * Comfortable with data platforms on AWS or GCP with an ability to deploy and scale batch jobs or always-on models. * Causal inference expertise: difference-in-differences, synthetic control, interrupted time series, matching methods. * Solid SQL and hands-on experience with a cloud data warehouse (BigQuery preferred). * The ability to communicate with non-technical stakeholders clearly enough that they can act on your outputs. Explaining the model is as important as building it. * AI-native ways of working: you use AI agents and tools as a genuine accelerator in your workflow, for analysis, code generation and data exploration. You evaluate model outputs critically, know when to push back, and are actively extending what you can do independently through agentic approaches. This is a core expectation of the role, not an optional extra., * Experience in marketing analytics: customer LTV, media mix modelling or channel attribution in an e-commerce or subscription context. * MLOps experience: model versioning, deployment pipelines, monitoring and retraining workflows. * Familiarity with the GCP ecosystem (BigQuery, Vertex AI, Cloud Run). * Experience presenting to senior commercial stakeholders or embedding models into business planning cycles. * Background in e-commerce, subscription or retail where customer behaviour data is central to the business. ## Description This is an applied full stack data science role partnering closely with the Customer team. You will not just design models. You will build and ship them. The work spans customer lifetime value, survival analysis, incrementality testing and channel mix: problems that are technically demanding and commercially consequential. You will work directly with marketing and commercial stakeholders to frame the right question, build the right model, and translate outputs into decisions. This is a senior individual contributor role. Your influence is through technical excellence and thought leadership, not line management. The expectation is that you raise the bar for how data is used in Marketing decisions and, over time, help define what rigorous applied data science looks like at Pet Circle more broadly. You will partner closely with the Data Platform engineering team to get models into production and ensure outputs are surfaced reliably. What You'll Be Working On * Build and own lifetime value and churn models that drive how Marketing prioritises customer segments and allocates spend across acquisition and retention channels. * Design and run incrementality experiments, randomised controlled trials and quasi-experiments, that establish whether our marketing interventions actually cause the outcomes we attribute to them, and build the standards that govern how these experiments are set up, powered and interpreted. * Develop spend-curve and scenario planning models that translate channel investment into forecasted incremental outcomes, giving commercial and marketing teams the tools to make allocation decisions with confidence. * Work directly with marketing and commercial stakeholders to frame hypotheses, design clean tests and communicate results, including the uncertainty in them, in terms non-technical audiences can act on. * Productionising models, monitor for drift and maintain reliability over time, with a clear path from prototype to production. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [TresJS a new declarative ThreeJS as Vue components](https://www.wearedevelopers.com/videos/543-tresjs-a-new-declarative-threejs-as-vue-components) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Making Data Warehouses fast. 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