Senior Applied Data Scientist

At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.

Responsibilities

Define evaluation frameworks for AI agents (precision, recall, correctness, robustness); Perform error analysis on AI outputs (SQL generation, mappings, data products); Build and curate training and evaluation datasets (golden sets, edge cases, benchmarks); Design and tune model/prompt strategies for improving agent performance; Establish quality metrics for data quality agents; Establish quality metrics for data modeler / engineer agents; Establish quality metrics for mapping and enrichment workflows; Analyze and validate AI-generated artifacts: SQL queries; Analyze and validate AI-generated artifacts: Data models; Analyze and validate AI-generated artifacts: Data product outputs; Collaborate with AI Engineers to close the loop between evaluation → improvement

Requirements

Experience building and leading a data science team; Proven experience designing and evaluating AI/LLM-based systems, including agent-driven workflows; Experience building or contributing to agent-based architectures or AI-enabled systems; Hands-on experience implementing governance, safety, and quality controls in AI environments; Strong ability to collaborate with product leaders, architects, and engineering teams to define enterprise-grade AI capabilities; Proficiency in Python; Experience with ML/NLP or LLM evaluation workflows; Strong understanding of precision / recall / F1 / ranking metrics; Strong understanding of statistical validation and experimentation; Strong foundation in Machine Learning (ML); Strong foundation in Deep Learning (DL); Strong foundation in Deep/Reinforcement Learning (D/RL); Strong foundation in Applied Data Science; Strong knowledge of statistics; Strong knowledge of statistical inference; Strong knowledge of probability and data analysis concepts; Experience building evaluation datasets and benchmarks; Ability to debug model behavior; Ability to identify failure modes; Strong Python + data analysis skills; Experience with SQL and data systems; Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field; 3–5+ years of hands-on experience; Strong proficiency in Python and experience working with data-intensive systems; Experience working with modern AI/LLM ecosystems (e.g., prompt engineering, evaluation workflows, or agent tooling); Familiarity with distributed systems and cloud-native environments (AWS, Azure, or GCP); Strong SQL skills and the ability to understand and trace complex data transformations; Hands-on experience with data modeling, data quality, and data lineage concepts; Familiarity with modern data transformation tools (e.g., dbt) and governed analytics layers; Understanding of metadata management and enterprise data governance practices; Strong communication skills and the ability to collaborate across engineering, data, and product teams; Passion for building trusted, high-quality, and well-governed data foundations for AI systems

Benefits

People-first culture; Flexible work model; Focus on well-being; Inclusive environment