Jobs at Fetcherr

Architect and build a Kubernetes-native self-service platform on GCP: create controllers/operators, enforce everything-as-code (Terraform/Helm/GitOps), own production reliability with observability, SLOs and alerting, and enable secure, automated developer workflows.

Provide frontline technical support for data pipelines and the Fetcherr platform, monitor production workflows, troubleshoot data-related issues, communicate with stakeholders, maintain production schedules and records, and coordinate with Data, R&D, and Product teams to ensure SLA compliance.

Manage strategic enterprise accounts as primary client contact, ensuring satisfaction, commercial performance, renewals, product adoption, and alignment with client objectives. Coordinate cross-functional teams, analyze client metrics, identify upsell/cross-sell opportunities, influence product development with client feedback, and travel for meetings and QBRs.

Design and implement the Price Optimizer's decision logic: translate pricing rules into mathematical models and production-ready Python code, develop and tune simulation and optimization algorithms, write tested maintainable code, analyze model behavior and edge cases, and collaborate with solution architects and data and engineering teams to integrate models into Dagster pipelines.

Design and implement the decision-making logic of a Price Optimizer: translate pricing rules into mathematical models and production-ready Python code, refine simulation and optimization algorithms, write tested maintainable code, validate model behavior with data, and collaborate with Solution Architects and Data Engineers to integrate models into Dagster pipelines.

Design and implement decision-making logic for a price optimizer: translate pricing rules into mathematical models and production-ready Python code, refine simulation and optimization algorithms, validate model behavior using data, and collaborate with architects and data engineers for pipeline integration.

Design and implement pricing decision logic and optimization models in production-quality Python. Translate business rules into mathematical constraints, validate model behavior with data, collaborate with Solution Architects and Data Engineers to integrate models into Dagster pipelines.

Design and implement the Price Optimizer decision logic: translate pricing rules into mathematical models and production-ready Python code, refine simulation and optimization algorithms, write tested maintainable code, validate model behavior, and collaborate with Solution Architects and Data Engineers to integrate into Dagster pipelines.

Build and deploy econometric and ML models for demand forecasting, research novel approaches to improve accuracy and scalability, collaborate with product and engineering to productionize models, and communicate technical findings to non-technical stakeholders.

Develop and implement econometric and machine learning models for demand forecasting; research and evaluate novel approaches to improve accuracy, robustness, and scalability; collaborate with product, data engineering, MLOps and platform teams to deploy ML systems in production; communicate findings to non-technical stakeholders.

Design and implement econometric and machine-learning demand-forecasting models, run research and experiments to improve accuracy and scalability, collaborate with product, data engineering and MLOps teams to deploy models to production, and communicate technical results to non-technical stakeholders.

Build and deploy econometric and machine learning models for demand forecasting. Research and test novel approaches to improve accuracy and scalability. Collaborate with product, data engineering, MLOps, and platform teams to productionize ML systems. Communicate technical findings to non-technical stakeholders.

Design, research, and deploy econometric and ML models for demand forecasting. Improve model accuracy, robustness, and scalability; collaborate with product, data engineering, and MLOps; communicate technical results to non-technical stakeholders.

Manage strategic enterprise client relationships to ensure satisfaction, commercial performance, product alignment, renewals, and adoption. Coordinate cross-functional teams, analyze client performance metrics, identify expansion opportunities, and represent client needs to influence product and service improvements. Travel to client meetings and industry events as needed.

Manage strategic client relationships across verticals, drive product adoption and commercial performance, coordinate cross-functional teams for delivery, support renewals, analyze client metrics, and represent client needs to influence product development. Travel for client meetings as needed.

Maintain and scale production ML systems: monitor model performance and explainability, automate MLOps pipelines, troubleshoot production workflows, build feature and inference monitoring, perform EDA on historical time-series data, and support internal tools and deployment-specific adjusters.

Maintain and scale production ML systems: monitor model performance and explainability, automate MLOps pipelines, troubleshoot production workflows, perform EDA and data quality checks, build and maintain static and temporal features, support feature engineering and artifact monitoring, and develop internal tools and registries to ensure reliable, explainable ML deployments.

Build and maintain scalable ML models and data pipelines for training and inference. Ensure data accuracy and efficiency across structured and unstructured sources, using orchestration, containerization, and distributed systems to support data science workflows.

Build and maintain models and data pipelines for training and inference. Ensure data accuracy, consistency, and scalability across structured and unstructured sources. Implement pipeline orchestration, containerized deployments, and distributed computing to support data science workflows and production ML systems.

Build and maintain scalable ML models and data pipelines for training and inference. Ensure data accuracy and efficiency across structured and unstructured sources, work with orchestration, containerization, and distributed systems to support production ML workflows and collaboration with data science teams.