In this role, you will help create the full data lifecycle that underpins our models: from designing what data we collect, through curation and quality monitoring, to running rigorous experiments that drive model improvements. You will work closely with other ML and Data Engineering teams to ensure our models are trained on the best possible data, reaching the best accuracy, and that we deeply understand when and why they don't perform as expected.
Responsibilities
As an ML Data Scientist on this team, you will play a central role in shaping the data that powers our ML models. You will:
Investigate model failures – identify patterns, hypothesize root causes, and work with the team to implement fixes
Own data curation: evaluate, clean, and curate datasets to maximize model training quality
Design and execute experiments end-to-end: from defining the question and data collection escort, through analysis and statistical validation, to presenting clear conclusions and driving implementation
Define data collection strategies – collaborate with others to decide what data we should be collecting and why
Design and maintain monitoring solutions with others to ensure ongoing data quality and integrity at scale
Minimum Qualifications
M.Sc. in Computer Science, Electrical Engineering, Computational Biology/Neuroscience, Mathematics, Statistics, or a related field.
5+ years of industry experience in applied machine learning, data science, or a related field.
Strong hands-on experience with Python, PyTorch and SQL for large-scale data analysis and pipeline development.
Hands-on experience with the full ML experimentation cycle: problem definition, data collection, statistical analysis, and conclusion-driven iteration.
Proven ability to analyze model failures and translate findings into concrete improvements.
Strong analytical thinking and ability to independently define and drive research directions.
Excellent cross-functional communication skills – ability to work effectively with other ML and Data Engineers.
Preferred Qualifications
Experience with applied speech, audio or signal processing ML systems.
Experience with data-efficient training strategies.
Experience with continual or online learning.
Familiarity with data quality frameworks, monitoring pipelines, and data validation at scale.
Strong statistical foundation – hypothesis testing, uncertainty quantification, evaluation metrics design.
Ph.D. n Computer Science, Electrical Engineering, Computational Biology/Neuroscience, Mathematics, Statistics, or a related field.
