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Role Title: AVP, Applied Model Ops Developer (L11) Company Overview: Synchrony (NYSE: SYF) is a leading consumer financing company that has been at the heart of American commerce and opportunity for nearly a century. Synchrony delivers credit and banking products that empower tens of millions of consumers to improve their financial lives and access what matters most. Leveraging innovative solutions that are shaping the future of retail commerce, Synchrony supports the growth and success of some of the nations most respected brands, alongside hundreds of thousands of small and midsize businesses, including health and wellness providers. Committed to excellence in service and culture, Synchrony is proud to be named as #3 as a Great Place to Work in India and is honored to be ranked the #1 Best Company to Work For in the U.S. by Fortune magazine and Great Place to Work. For more information, visit www.synchrony.com. Organizational Overview: Our Analytics organization comprises of data analysts who focus on enabling strategies to enhance customer and partner experience and optimize business performance through data management and development of full stack descriptive to prescriptive analytics solutions using cutting edge technologies thereby enabling business growth. Role Summary / Purpose : The AVP, Applied Model Ops Developer within the India Analytics Hub (IAH), operating under the Decision Management, Model Operations & Analytics team is responsible for designing and building the data infrastructure, pipelines, and tooling required to support robust, scalable, and automated post-deployment monitoring of models. This role bridges the gap between data science and software engineering by building, deploying, and maintaining production-ready AI/ML systems. The engineer collaborates closely with model developers, product managers, risk partners, and compliance teams to operationalize monitoring strategies aligned with model governance policies. Key Responsibilities: Engage regularly with model developers, validators, and risk stakeholders to understand their evolving data needs for model development, monitoring, and governance. Partner with credit analytics, risk, fraud, marketing, and operations functions to identify, define, and prioritize use cases requiring model-ready data. Build scalable data architectures to support real-time and batch monitoring, including data ingestion, enrichment, and retention practices.Support pipeline development by designing and maintaining automated end-to-end ML pipelines for data collection, preprocessing, feature engineering, and model training.Conduct data transformation by converting raw observations into variables (features) that machine learning models can understand, such as turning timestamps into cyclical time features. Transforming theoretical data science prototypes into robust, high-performance software systems that can handle large volumes of real-time dataCI/CD Pipeline Development: Build and maintain automated pipelines that handle not just code, but also data validation, model training, and artifact managementDesign, develop, and maintain robust pipelines to collect, transform, and store data used in model monitoring workflows (e.g., scoring data, performance metrics, outcomes).Provide thought and technical leadership in generating new signals from raw data by applying techniques such as normalization, scaling and categorical encodingIntegrate data pipelines with model lifecycle platforms, MLOps tools, and observability solutions to ensure seamless model performance tracking.Partner with model risk and compliance teams to ensure data lineage, audit trails, and documentation are preserved and accessible for regulatory reviews (e.g., SR 11-7 compliance).Liaise with cloud, data lake, data warehouse, and model governance engineering teams on delivery execution and backlog prioritization. Collaborate with data scientists, model validators, and product managers to align monitoring data infrastructure with evolving model monitoring requirements.Optimize data storage and compute performance for large-scale monitoring use cases involving high-frequency scoring or model ensembles. Required Skills & Knowledge: Bachelors degree in a quantitative, technical, or data-focused field (e.g., Statistics, Mathematics, Computer Science, Data Science, Engineering) with 6+ years experience OR in lieu of a degree 8+years of relevant work experience in monitoring, validation, or credit risk strategyMinimum 6+ years of professional experience in model operations, data engineering, or analytics infrastructure Strong proficiency with data engineering tools and frameworks (e.g., Apache Spark, Airflow, Kafka, dbt, PySpark).Proficient in programming languages such as SAS, Python, and SQL for building monitoring pipelines and validation checks.Experience with cloud-based data infrastructure (e.g., AWS, Azure, GCP) and data warehousing (e.g., Snowflake .
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