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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** TimeLeft LLC - **Location:** United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, BigQuery, Code Review, Continuous Integration, Data Flow Control, Machine Learning, Tensorflow, Azure Machine Learning, SQL Databases, Reinforcement Learning, Pytorch, Deep Learning, Git, Xgboost, Apache Kafka, Marketplace - **Published:** August 30, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pg4di9yrzm ## About the Role * Strong Python for data science and ML (scikit-learn, XGBoost/LightGBM; PyTorch or TensorFlow a plus if deep learning is relevant to future use cases). * Proven track record shipping models to production, not just modeling in a notebook. Can talk through at least one model that served live traffic. * Hands-on experience with a cloud ML platform, ideally GCP (Vertex AI, BigQuery ML, Cloud Run/Functions) or fast ability to translate equivalent AWS/Azure experience. * Solid SQL; comfortable working against a dbt/BigQuery warehouse. * Software engineering fundamentals: git, code review, testing, CI/CD: You'll be shipping code Engineering has to trust. * Causal inference / uplift modelling or applied experimentation experience: pricing and discounting need "what if we hadn't," not just "who churns." Nice to have: * Experience with streaming/event pipelines (Pub/Sub, Dataflow, Kafka): useful as we move off pure batch. * Experience with pricing, discounting, personalization specifically. * Familiarity with feature stores or the DIY equivalent (versioned feature pipelines). * Multi-armed bandits or reinforcement learning for pricing/personalization. * Startup experience: comfortable being the first person to build something rather than joining an existing ML platform team. Soft skills * Genuinely energised by "does this move the metric," not just "is this model accurate." * Can hold their own in a room with Engineering and with Business: Speaks commercial as well as the language of engineering * Explains modeling tradeoffs in plain business terms to Product/leadership without dumbing it down or using too much jargon * Comfortable owning ambiguity - this role is defining the pattern, not following one. Required experience * 4-7 years in a data scientist / ML engineer role, with at least one model you personally took from prototype to live production serving real users or real traffic. * Quantitative background (CS, stats, engineering or equivalent hands-on experience). * B2C, subscription, or marketplace experience is a strong plus * Fluent English. ## Description The Data Scientist is the first hire on the team whose job is to put machine learning into production, not just into a notebook. You'll build models that power product and other live flows sometimes surfaced to users in real time, not just reported on in a dashboard a week later. The first flagship project is about personalization across the user journey: a model that decides, per user, what offer to show, wired directly into the product and lifecycle experience rather than sitting in a warehouse table. From there, you'll extend the same muscle: model * API * product surface to other high-leverage moments in the user journey. You'll work hand-in-hand with Product, Engineering and Lifecycle marketing to ship models as features, not as reports. This is also a foundational role for the team's infrastructure: most of what Data does today is batch (dbt, Lightdash, BigQuery); you'll help establish our first real-time low-latency serving patterns on GCP and work closely with engineering on this. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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