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Senior Data Scientist
Native
- New York, New York, United States
- New York, New York, United States
About
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 #J-18808-Ljbffr
Languages
- English
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