You will own and maintain cloud infrastructure on AWS using Terraform and manage production-grade Kubernetes clusters. Additionally, you will automate operational workflows, build CI/CD pipelines, and ensure high availability for Data and AI platforms.
The Head of Marketing will define and execute the global marketing strategy across the US and LATAM markets to drive revenue and pipeline growth. They will lead a senior team of specialists, manage the marketing budget, and ensure alignment between marketing initiatives and sales objectives.
Design and build an agentic AI architecture to generate and prioritize actionable insights for sales leaders. Model coaching route recommendations as an optimization problem to balance geographic proximity, route priority, and operational constraints.
Design and build an agentic AI architecture to generate and prioritize actionable sales coaching insights. Model coaching routes as an optimization problem to balance geographic proximity, business priority, and operational constraints.
The role involves co-developing batch and streaming pipelines on Databricks while defining data contracts and quality rules. Additionally, the engineer will mentor domain teams on data modeling and best practices to improve platform templates and tooling.
You will mathematically formalize the trade-off between service costs and revenue to optimize customer service models. Additionally, you will collaborate with cross-functional teams to build and scale characterization models across multiple countries.
Design, develop, and maintain backend services and REST APIs using Python and FastAPI within an Azure environment. Integrate messaging queues and data processes while ensuring system reliability, observability, and clean code standards.
The role involves co-developing batch and streaming data pipelines on Databricks and defining data contracts and quality rules. You will also mentor domain engineers on data product ownership and best engineering practices.
Design and implement transactional and credit behavior features for risk models using standard metadata and governance practices. Ensure consistency between training and production environments while managing deployment pipelines and job monitoring.
The role involves developing batch and streaming data pipelines on Databricks while defining data contracts and quality rules. Additionally, the engineer will mentor domain teams on data modeling and best practices to improve platform templates and tooling.
You will mathematically formalize the trade-off between service time and revenue to optimize customer service models. Additionally, you will build and solve optimization models while collaborating with business, finance, and engineering teams.
Design, develop, and maintain backend services and REST APIs using Python and FastAPI within an Azure environment. Collaborate with cross-functional teams to integrate messaging queues, databases, and data processes while ensuring system reliability and observability.
Design and analyze experiments to measure the causal impact of marketing campaigns and business initiatives. Collaborate with cross-functional teams to translate business questions into analytical models that drive growth and revenue.
Design and analyze experiments to measure the causal impact of marketing campaigns and business initiatives. Collaborate with cross-functional teams to develop predictive models and data-driven strategies that optimize customer acquisition and revenue.
Design and implement scalable Machine Learning and AI solutions to enhance digital customer experiences and business outcomes. Collaborate with cross-functional teams to develop predictive models for churn, personalization, and customer behavior analysis.
The lead will serve as a primary technical consultant to execute end-to-end AI/ML projects while designing and maintaining production-grade pipelines. They will also manage infrastructure and contribute to the strategic growth of the AI practice.
The role involves serving as a technical consultant to lead end-to-end AI/ML project implementations and translating business problems into technical solutions. You will also design, build, and maintain production-grade AI/ML pipelines while managing infrastructure and contributing to the strategic growth of the AI practice.
You will orchestrate production pipelines on Databricks, managing deployments via Asset Bundles and ensuring end-to-end operational reliability. Additionally, you will manage model lifecycles in MLflow and Unity Catalog while diagnosing runtime incidents in collaboration with development squads.
Orchestrate production pipelines on Databricks and manage model lifecycles using MLflow and Unity Catalog. Ensure operational continuity by managing compute resources, cost-monitoring tags, and coordinating with development squads.
You will co-develop batch and streaming data pipelines and semantic models using Databricks while defining data quality rules and SLOs. Additionally, you will mentor domain teams on data product ownership and engineering best practices to ensure scalable and auditable AI solutions.
Lead end-to-end data migration initiatives and define data architecture decisions across cloud platforms. Act as the primary liaison between engineering, product, and business stakeholders to ensure project alignment and successful delivery.
The role involves co-developing batch and streaming pipelines on Databricks while defining data contracts and quality rules. You will also mentor domain teams on data modeling and engineering best practices to improve platform templates.
