• Improve accuracy of our production machine learning systems by applying the latest published research, feature engineering, and tuning of model hyperparameters • Own end-to-end accuracy and performance for our 60+ machine learning problems • Be up to date on the latest research publications, and rapidly assess the latest results for production and scalability potential • Work closely with product leadership and customers on training datasets and error analysis

- Monitor and manage daily operational activities to ensure efficiency and quality standards - Analyze operational processes and identify areas for improvement - Coordinate with cross-functional teams including Product, Development, and Customer Support - Track KPIs and prepare regular performance reports for management - Assist in implementing new systems, tools, and workflows - Handle operational issues and provide timely resolutions - Maintain accurate documentation of processes and procedures - Support project execution and ensure deadlines are met

• Applying Data Science and Machine Learning best practices to develop robust models and support data-driven decision-making across business domains • Applying machine learning and data science techniques such as forecasting, predictive modeling, classification, regression, recommendation, and optimization to solve business problems • Conducting experiments and evaluating models using appropriate statistical, technical, and business performance metrics • Architecting, building, deploying, and maintaining scalable machine learning models and AI solutions integrated into enterprise systems, applications, and operational workflows • Designing and implementing end-to-end ML workflows, including data preparation, feature engineering, model training, validation, deployment, optimization, and continuous monitoring in a high-scale production environment • Developing reusable machine learning components, feature pipelines, and model-serving frameworks to support multiple use cases and teams • Designing and implementing production-grade MLOps solutions using Azure ML, Databricks, MLflow, and related cloud technologies • Building and maintaining automated ML pipelines, feature engineering workflows, feature store patterns, and deployment processes for training, testing, monitoring, and retraining machine learning models • Implementing standards and best practices for model versioning, lifecycle management, governance, deployment automation, model performance monitoring, drift detection, data quality, operational health, and retraining triggers • Developing production-quality Python code, APIs, automation workflows, and machine learning services to integrate ML capabilities into business applications and processes.

• Design, build, and deploy machine learning models and AI systems in production environments • Develop components such as Model inference services, Data and feature pipelines, Complex recommendation and matching services, Vision based analysis systems, Evaluation and monitoring pipelines • Optimize models for performance, reliability, and cost efficiency • Build systems that integrate with enterprise tools and APIs • Write high-quality, maintainable, and well-tested code • Participate in architecture design and technical reviews • Partner with product, data engineering, and infrastructure teams • Help identify high-impact AI use cases within portfolio companies