• Technical leadership and end-to-end ownership of pricing and revenue ML initiatives • Develop and optimize forecasting and pricing models • Combine time series, demand signals, and heterogeneous data sources • Build a robust measurement system and define clear rollout criteria • Establish standards for backtesting, reproducibility, and versioning • Ensure operational reliability through monitoring, drift detection, and retraining mechanisms • Automate high-impact processes to improve throughput and quality • Design data models in the data warehouse to serve as the basis for reliable metrics • Standardize dashboards for business KPIs • Prioritize requirements together with Product and Revenue teams

• Model Evolution: You develop and optimize our forecasting and pricing models as well as data-driven decision logics, always methodically pragmatic and fully impact-focused. • Signal Hunting: You work with time series, demand signals and heterogeneous data sources. You define features and labels so cleanly that leakage has no chance. • Measurement & Guardrails: You are responsible for evaluation through backtests, robust metrics and segmentations. You support holdouts and A/B setups and ensure the checks between offline and online performance. • Data Visibility: You build dashboards and reports that make model and business KPIs transparent. Your focus is always on the highest data quality. • Smart Workflows: You drive reproducible workflows (versioning, clean pipelines, meaningful tests) and automate recurring analyses and evaluation runs.

• Develop and manage scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks • Design and implement robust model serving infrastructure using platforms like TorchServe, TensorFlow, Triton etc. • Develop scalable inference architectures optimized, with ultra-low latency and high throughput • Ensure seamless model deployment by implementing A/B testing, canary releases, and rollback capabilities • Develop logging, alerting, and monitoring solutions to track model development, and reliability • Improve GPU usage, enable autoscaling, and streamline resource allocation to boost efficiency • Design, implement, and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data

• Lead and mentor a team of MLOps engineers, fostering technical growth and a culture of operational excellence • Define and drive the MLOps roadmap, aligning infrastructure capabilities with Research, Engineering and product objectives • Establish best practices, standards, and processes for ML infrastructure, deployment, and operations • Own technical decision-making for ML infrastructure architecture and tooling choices • Architect and oversee scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks • Drive the design and implementation of robust model serving infrastructure using platforms like Triton, TorchServe, TensorFlow Serving, and KServe • Define inference architecture strategy optimized for ultra-low latency and high throughput • Design and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data • Collaborate with research teams to bridge the gap between experimentation and production • Define logging, alerting, and monitoring strategy to track model performance, drift, and system reliability