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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist - **Company:** Native - **Location:** New York, NY, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Unit Testing, Distributed Data Store, Python (Programming Language), Tensorflow, SQL Databases, Unstructured Data, Google Cloud, Pytorch, Large Language Models, Scikit Learn, Build Tools, Machine Learning Operations, Data Pipelines, Web Api - **Published:** August 31, 2026 - **Apply:** https://www.careerboard.com/us/en/find-jobs-in-United-States/-DDDC99E2FD90090A95/ ## About the Role * Raw talent: Demonstrated success in building and deploying AI/ML systems that operate in production at scale. * Technical Mastery: Deep fluency in Python or R or SQL, distributed data systems, and ML frameworks (eg, TensorFlow, PyTorch, Scikit-learn, vetiver, tidymodels). Nice-to-have; Airflow, Vertex AI, GCP Dataforms * MLOps & AI Proficiency: Hands-on experience with unstructured data pipelines and LLM integration for Real Time inference. Experience implementing API endpoints or at least data pipelines/workflows within Google Cloud Platform in Dataforms. * Operational Rigor: Ability to deliver reliable systems under constraints-limited resources, ambiguous inputs, and high-pressure timelines. Experience with some form of code modularization and unit testing. * Commercial Awareness: Familiarity with how CPG manufacturers and distributors execute in the market, and how data translates into demand planning, distribution, and retail execution. (Not a deal breaker) * Velocity and Precision: Bias toward decisive action, measured by speed of deployment and model accuracy in the field. * Scalable Value Delivery: Build models that drive repeatable outcomes, not bespoke analysis. MLOps experience on actual implementations will be highly regarded. * No Credentialism: Degrees, pedigrees, and credentials are irrelevant. What matters is capability; decisive executors who operationalize AI and deliver intelligence-grade results. ## Description * Own the Data: Command the full lifecycle of data pipelines - ingestion, cleaning, structuring, and analysis of large-scale, noisy, analog signals. * Operationalize AI: Design, train, and deploy ML/AI models (including LLMs, predictive systems, and demand-forecasting models) into production environments. * Execution at Velocity: Move from prototype to deployment with speed, reliability, and measurable accuracy. * Model for Impact: Build systems that optimize quality control performance and decrease latency or deliver intelligence that drives customer growth with operational leverage. * Domain Partnership: Work directly with Engineering, Product, and Commercial teams to ensure models translate into measurable outcomes, not academic outputs. * Evolve the Platform: Advance the intelligence layer that makes the world's largest commercial channel legible and actionable. * Performance is assessed on one axis: The velocity, precision, and scale at which data science converts fragmented analog signals into decisive market intelligence. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)