What success might look like: Success in this role means independently leading the most challenging parts of data science engagements, producing reliable and scalable solutions, and serving as a trusted technical advisor to product managers, engineers, and business stakeholders. You will be recognized for your ability to translate complex business problems into well-scoped analytical strategies, and for consistently raising the technical bar across the team.
Who You’ll Work With
An outline of the team: You will be part of a collaborative and dynamic data science team that works closely with data engineers, project managers, solution architects, and business stakeholders. You will contribute as a high-level technical expert, helping teams navigate complexity and ensuring that analytical solutions are sound, scalable, and aligned with business needs.
The role and the team play at Teradata: The data science team at Teradata plays a crucial role in transforming data into strategic assets. As a Principal/Staff Data Scientist, you will influence the technical direction of projects, contribute reusable methodologies, and help shape best practices for developing advanced analytical solutions across multiple engagements.
Who the position reports to: This position reports to the Data Science Team Manager.
What Makes You a Qualified Candidate
Non-negotiable qualifications
Minimum of 8 years of experience in data science, machine learning, or applied analytics.
Expertise in programming languages such as Python and SQL.
Strong understanding of advanced statistical methods, machine learning, deep learning, and GenAI techniques.
Proven ability to scope, architect, and technically lead complex data science projects without direct management responsibilities.
Excellent communication and presentation skills, with the ability to engage both technical and non-technical audiences.
What You’ll Bring
Advanced Data Analysis and Modeling: Perform highly complex data analysis and build sophisticated predictive and prescriptive models using advanced statistical, machine learning, and deep learning techniques.
Technical Leadership: Define analytical approaches, scope data science components in partnership with PMs, and lead the technical direction of projects across multiple industries.
Cross-Functional Collaboration: Work closely with data engineers, product managers, and business stakeholders to align on requirements, solution direction, and implementation plans.
Data Exploration: Explore large and diverse datasets to uncover insights, evaluate feasibility, and guide model and feature design.
Model Deployment: Collaborate with engineering teams to implement, optimize, and maintain machine learning models in production environments, ensuring scalability, performance, and reliability.
Mentorship: Provide informal technical mentoring, conduct deep code/model reviews, and guide other data scientists in best practices—without direct people management.
Communication: Present complex findings, trade-offs, and solution paths to technical teams, business partners, and customer stakeholders in a clear and concise manner.
Continuous Improvement: Stay current with emerging data science, machine learning, and GenAI advancements, applying new techniques to enhance solution quality and accelerate delivery.
Why We Think You’ll Love Teradata
We prioritize a people-first culture because we know our people are at the very heart of our success. We embrace a flexible work model because we trust our people to make decisions about how, when, and where they work. We focus on well-being because we care about our people and their ability to thrive both personally and professionally. We are an anti-racist company because our dedication to Diversity, Equity, and Inclusion is more than a statement. It is a deep commitment to doing the work to foster an equitable environment that celebrates people for all of who they are.
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