Role Title

AVP, Applied Model Ops Developer (L11)

Job Summary

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 data.
- CI/CD Pipeline Development: Build and maintain automated pipelines that handle not just code, but also data validation, model training, and artifact management.
- Design, 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 encoding.
- Integrate 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

- Bachelor's 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 strategy
- Minimum 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, Redshift, BigQuery).
- Familiarity with MLOps practices, model metadata tracking (e.g., MLflow), and monitoring toolkits (e.g., Evidently AI, WhyLabs, Prometheus).
- Understanding of model risk governance requirements and the role of data engineering in ensuring compliant model monitoring.
- Ability to work in an agile workplace and deliver high-quality, production-grade code in collaboration with DevOps and platform engineering teams.

Desired Skills & Knowledge

- Advanced Master's degree or relevant advanced certification preferred
- Strong problem-solving skills and experience automating repetitive data monitoring tasks.
- Attention to detail and commitment to maintaining high standards of data quality, integrity, and compliance.
- Experience building alerting mechanisms and diagnostic logging for monitoring model behaviors.
- Excellent communication skills, with the ability to explain complex technical concepts to non-technical audiences and collaborate across teams.
- Exposure to explainability frameworks and the role of data in enhancing model transparency and interpretability.

Eligibility Criteria

Work Timings

This role qualifies for Enhanced Flexibility offered in Synchrony India and will require the incumbent to be available between 06:00 AM Eastern Time - 11:30 AM Eastern Time (timings are anchored to US Eastern hours and will adjust twice a year locally). This window is for meetings with India and US teams.

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 AVP, Applied Model Ops Developer (Hyderabad)
🏢 Synchrony
📍 Hyderabad