Senior Data Scientist

Title: Senior Data Scientist - Pipeline Automation (MLOps / DataOps Engineer) Mission Every consumer on earth purchases in one of three places: online, big-box retail, or mom-and-pop shops. Paradoxically, the largest commercial channel is, by far, humble traditional trade shops. Yet they remain fragmented, offline, and opaque. Native is the first intelligence-grade system built to penetrate this opacity. Each analog store is digitized into a dynamic graph where noise is filtered into low latency signals, transforming the antiquated offline world into advanced digital intelligence. Commercial leaders gain the precision to see what others cannot, store by store, rendering decisive decision advantage to win the market. Join the ground floor of the only platform engineered to decode the analog economy into operational dominance. Talent Values - High Leverage: Consistent ability to attain the productive capacity of 5-10 people through grit, raw talent, and sheer force of will. High Agency: Relentless sense of ownership in the outcome, regardless of circumstance, acting decisively to shape the environment rather than being shaped by it. Curiosity With Discipline: An evidence seeking, measurement mindset without succumbing to analysis paralysis, and a penchant for experimentation. Intellectual Honesty: Certain enough to act, humble enough to always be learning Role - 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. Requirements - 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 (e.g., 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. Company Native is a focused spinout backed by Vista, a $100B fund backing leaders in Artificial Intelligence and advanced technologies. Its mandate is to build the first intelligence-grade system for the world's largest and least-understood channel of trade. It's doing this by reverse-engineering analog markets into a digital graph, delivering precision, clarity, and control at enterprise scale. It is headquartered in New York City, with offices in Mexico City and Bogotá. Location Ciudad de Mexico, Mexico or Bogotá, Colombia.