Software Engineer - Data Quality

Overview

Join the founding team of the Truveta India Development Center to build AI-based anomaly detection and monitoring systems that keep Truveta's healthcare data at high quality.

What you'll do

• Build performant AI-based anomaly detection and monitoring systems, using machine learning and supporting statistical methods to analyze big data at scale.
• Design, implement, test, deploy, operate, and tune the online services, automation, and tooling that monitor Truveta's data, contributing hands-on to every stage of the software development lifecycle.
• Detect and characterize data-quality issues such as missing or late feeds, volume and distribution shifts, schema and contract drift, duplication, and implausible clinical values.
• Develop and evaluate detection approaches, from statistical baselines and time-series methods through machine-learning models, and measure them on precision, recall, and alert usefulness rather than accuracy alone.
• Turn detection output into signals people can act on: alerts with root-cause context, data-quality metrics, and dashboards that engineering and clinical teams trust.
• Work with data-processing engineers to build quality checks into pipelines so that defects are caught where they are introduced.
• Collaborate with cross-functional teams including product managers, clinical informaticists, architects, and other engineers to define what quality means for each data domain.
• Take part in design reviews, code reviews, and the on-call rotation for the services you own.

What you'll need

• Bachelor's degree in Computer Science or a related field, or equivalent practical experience.
• 1+ years of experience building production-quality services on a cloud platform such as Azure, AWS, or GCP.
• 2+ years writing Python for machine learning, AI-based data analytics, or statistical modeling.
• Hands-on experience across the software development lifecycle: design, implementation, testing, deployment, operations, and performance.
• Working knowledge of the statistics and model-evaluation methods that underpin that modeling work.
• Strong problem-solving skills and the ability to learn a new domain quickly.
• Clear written and spoken communication, and comfort collaborating across disciplines.

Nice to have

• Experience developing and evaluating AI or machine-learning approaches for anomaly detection, with supporting knowledge of statistical methods, time-series analysis, or data-quality monitoring.
• Experience working as part of distributed teams, particularly across India and the United States.
• Experience processing large datasets with Spark, Databricks, or comparable distributed frameworks, and strong SQL.
• Experience running services on Microsoft Azure.
• Familiarity with data-quality and observability tooling such as Great Expectations, Deequ, Soda, or equivalent in-house frameworks.
• Experience operating machine-learning models in production, including drift monitoring and retraining.
• Exposure to healthcare, clinical, or other regulated data.

Details

• Location: Hyderabad, India.