Machine Learning Validation & Data Operations Manager

Job Description

Department BSD SUR – OHNS: Thirty Million Words – Tech About the Department The TMW Center develops, tests, and implements evidence-based interventions designed to promote very young children’s cognitive and social-emotional development, with a priority placed on that of children living in poverty. TMW Center interventions are designed to be overlaid onto existing health, education, and social service systems working at scale in a given community in order to meet families where they already are. The TMW Center has a robust research and development strategy that includes further development and testing of TMW interventions; harnessing technology to support behavior change, intervention engagement, and analysis; and furthering strategies to engage adult caregivers (parents, early educators and others) in the TMW Center’s interventions across the health, early learning, and social service sectors.

The TMW Center is partnering with the Connecticut Office of Early Childhood (OEC) to conduct multi-year field research in Connecticut infant and toddler child care settings. The research will use a novel technology to support teachers and demonstrate how teachers’ language inputs in birth-to-3 child care settings lead to positive child outcomes. Early pilots will generate learnings and inform a classroom implementation and professional development model. A subsequent randomized controlled trial will test whether the new professional development model positively impacts teacher knowledge and behavior, leads to more language interactions between teachers and children, improves job satisfaction and drives positive child outcomes.

Job Summary The TMW Center for Early Learning + Public Health (TMW Center) develops, tests, and implements evidence-based interventions designed to promote very young children’s cognitive and social-emotional development, with emerging technologies that enhance—rather than replace—the pivotal role that caregivers play in building healthy young brains. Although there is a rich body of research demonstrating the importance of the early language environment for maximizing early learning, there are very ways to assess the quality of those environments.

As a result, when it comes to nurturing their children’s brains, parents often feel like they are in a maze without a map. The TMW Center has launched a wearable device and an accompanying app that uses machine learning to measure and analyze a child’s language environment and provide real-time information as well as personalized feedback and guidance for enhancing that environment. This groundbreaking piece of technology gives parents and caregivers with information they need to engage in robust brain-building interactions — and helps deepen caregiver-child connection.

The TMW Center is looking for an ML Validation & Data Operations Manager to lead and monitor operations for continuous algorithm improvement and validation. This will involve guiding the development of data management systems and establishing data assessment strategies to enable efficient and scalable validation processes. The ML Validation & Data Operations Manager will also contribute to defining the vision for current and longer-term algorithm developments.

They will collaborate closely with our data science, research, and data management teams to coordinate workstreams, ensure a smooth integration of the developed systems and protocols within broader operations, and align with research developments. This position will report to the CTO, with guidance from the Scientific Director.

Responsibilities Leads data labeling effort to build ground-truth corpus for existing and future algorithms, including identifying data requirements and protocols. Determines validation criteria and metrics across models and settings/partnerships/use cases. Collaborates with data management teams, and application development teams to identify and capture the data necessary to perform validation.

Recruits, trains, and leads a team of data labelers. Ensures alignment between the validation roadmap and Center’s priorities. Establishes timelines and strategies for the validation of different algorithms.

Works with ML and engineering teams to develop and manage pipelines for continuous algorithm validation and optimization. Promotes advances in, and creative ML solutions for validation and data management enhancement (e. g.

, automation of protocols and training pipelines). Builds quality assurance processes to continuously assess reliability of data. Maintains comprehensive records of data sources, methodologies, and results.

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