Senior Applied Data Scientist

Our company:

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.

What You'll Do

In this role you will...

• 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
  • Data modeler / engineer agents
  • Mapping and enrichment workflows
• Analyze and validate AI-generated artifacts:
  • SQL queries
  • Data models
  • Data product outputs
• Collaborate with AI Engineers to close the loop between evaluation → improvement

Who You'll Work With

On our team, we...

• Are part of Teradata's global engineering organization, responsible for building the technologies that power VantageCloud, our unified data and AI platform.
• Operate at the intersection of cloud computing, advanced analytics, and AI-driven automation to help enterprises unify and analyze data across hybrid and multi-cloud environments.
• Solve highly complex challenges in scalability, performance, interoperability, and intelligent automation to enable customers to turn data into insights and innovation.
• Collaborate across research, architecture, platform engineering, and product teams to shape the future of enterprise AI.
• This position reports into the Knowledge & Autonomous Platform engineering leadership team within Teradata's global engineering organization.

What Makes You a Qualified Candidate

• 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
  • statistical validation and experimentation
• Strong foundation in:
  • Machine Learning (ML)
  • Deep Learning (DL)
  • Deep/Reinforcement Learning (D/RL)
  • Applied Data Science
• Strong knowledge of:
  • statistics
  • statistical inference
  • probability and data analysis concepts
• Experience building evaluation datasets and benchmarks
• Ability to:
  • debug model behavior
  • identify failure modes
• Strong Python + data analysis skills

• Experience with SQL and data systems

What You'll Bring

• 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

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