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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