Data Science - Forward Deployed Engineer: PhD Internship Opportunities

Job description

Prepare and assess data by using querying, visualization, and reporting techniques to identify key attributes, quality considerations, and project impact. Develop, train, optimize, and evaluate models using appropriate statistical and machine learning approaches to address business objectives. Design and analyze controlled experiments, communicate findings to stakeholders, and connect technical metrics with business outcomes. Partner with engineering teams to operationalize models, supporting performance, scalability, monitoring, maintenance, and integration into production systems. Collaborate with cross-functional partners to define evaluation metrics and improve artificial intelligence system and product performance. Currently pursuing a Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or a related field, with at least one academic quarter or semester remaining after completion of the internship. Must have at least one additional quarter/semester of school remaining following the completion of the internship. Candidate must be enrolled in a full time bachelor's, masters, MBA, or PhD program in area relevant for the role during the academic term immediately before their internship. 2+ years of programming experience in data science, machine learning, statistical computing, or a related technical area. 1+ year of experience delivering data science or machine learning projects in collaborative, cross-functional teams. 2+ years of experience developing and applying statistical methods, machine learning algorithms, data structures, or optimization techniques. Coursework, research, or applied experience in machine learning, statistical modeling, experimentation, causal inference, data mining, databases, data engineering, or distributed systems. Experience with SQL and programming languages commonly used in data science, such as Python, R, Java, Scala, C#, or similar. Familiarity with data pipelines, large-scale data processing frameworks, model evaluation, telemetry systems, visualization or reporting tools, or cloud-based data and machine learning platforms. Interest in large-scale data science, machine learning systems, experimentation, model optimization, data quality, and translating analytical findings into business and customer impact.