Senior Data Scientist - E-Commerce

Title:

Senior Data Scientist - Pipeline Automation (MLOps / Data Ops Engineer)
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. 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.
Relentless sense of ownership in the outcome, regardless of circumstance, acting decisively to shape the environment rather than being shaped by it.
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.
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.

Deep fluency in Python or R or SQL, distributed data systems, and ML frameworks (e.g., Tensor Flow, PyTorch, Scikit-learn, vetiver, tidy models). Airflow, Vertex AI, GCP Dataforms
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.

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. (Velocity and Precision:  Bias toward decisive action, measured by speed of deployment and model accuracy in the field.
Build models that drive repeatable outcomes, not bespoke analysis. 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.