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AVP, Applied Model Ops Developer
Location
India
Posted
82 days ago
Salary
0
Seniority
Lead
Job Description
AVP, Applied Model Ops Developer
Synchrony
• 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. • 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).
Job Requirements
- 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 environment and deliver high-quality, production-grade code in collaboration with DevOps and platform engineering teams.
Benefits
- best-in-class employee benefits and programs that cater to work-life integration and overall well-being
- career advancement and upskilling opportunities
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