Research Engineer / Research Scientist

Teradata Group; Ciudad de México; Presencial; MXN 400,000 - 600,000; Jornada completa

Descripción de la vacante

A leading analytics firm in Ciudad de México seeks a qualified candidate for advanced AI research. Responsibilities include identifying research opportunities, translating scientific concepts into prototypes, and communicating findings effectively. Ideal candidates have a PhD in a relevant field and demonstrated experience in building scientific systems. This role requires proficiency in Python and familiarity with ML frameworks, offering the chance to shape the future of data analytics.

Formación

• PhD in theoretical physics, applied mathematics, computational science, or related field, or 8+ years of equivalent experience.
• 5+ years of experience building and evaluating scientific systems.
• Proven ability to bridge theory with engineering through research.

Responsabilidades

• Identify research opportunities to strengthen product strategy.
• Translate scientific concepts into working prototypes.
• Communicate findings through technical reports and presentations.

Conocimientos

Proficiency in Python

Experience with ML frameworks

Strong expertise in mathematics

Advanced computational methods

Experience with scientific systems

Educación

PhD in a relevant field

Herramientas

NumPy

SciPy

PyTorch

Descripción del empleo

Our Company

At Teradata, we believe that people thrive when empowered with better information. That’s why we built the most complete cloud analytics and data platform for AI. By delivering harmonized data, trusted AI, and faster innovation, we uplift and empower our customers—and our customers’ customers—to make better, more confident decisions. The world’s top companies across every major industry trust Teradata to improve business performance, enrich customer experiences, and fully integrate data across the enterprise.

What You’ll Do

• Work at the intersection of advanced AI, mathematics, and domain science to identify emerging research opportunities that strengthen Teradata’s long-term product strategy.

• Translate complex scientific concepts into working prototypes, owning the full journey from theoretical formulation to implementation, experimentation, and proof-of-concept evaluation.

• Develop rigorous methodologies to assess new mathematical or algorithmic approaches against real-world constraints, product requirements, and performance metrics.

• Communicate research findings through clear documentation, technical reports, and presentations that influence strategic direction.

• Succeed by independently driving high-impact research that transforms foundational scientific insights into tangible value across multiple industries.

Who You’ll Work With

• Operate within Teradata’s Advanced Research organization, where we explore next-generation AI, scientific modeling, and computational approaches that inform the future of Teradata’s technology.

• Partner across product, engineering, and domain-focused teams to ensure research innovations can transition into prototypes and real-world applications.

• Support company-wide strategy by identifying emerging scientific methods that unlock new capabilities for the Teradata platform and its customers.

• This position reports to leadership within the Advanced Research team.

What Makes You a Qualified Candidate

• PhD in theoretical physics, applied mathematics, computational science, materials science, quantitative finance, or a related field, or 8+ years of equivalent industry research experience.

• 5+ years of experience building, implementing, and evaluating scientific systems or research prototypes.

• Proven ability to bridge theory with engineering through published research or demonstrable prototype development.

• Strong expertise in mathematics, statistical modeling, physics-based simulation, or advanced computational methods.

• Proficiency in Python, C++, or similar languages used to produce production-quality research code.

What You’ll Bring

• Experience with ML frameworks and scientific computing libraries such as NumPy, SciPy, JAX, or PyTorch.

• Ability to leverage modern AI tools (e.g., Claude, ChatGPT, coding assistants) to accelerate research and prototyping.

• A research-driven, hypothesis-oriented mindset combined with the ability to own full-stack implementation and experimental validation.

• Strong ability to translate complex concepts into concrete objectives and measurable experiments.

• Comfort operating autonomously on long-horizon research efforts with minimal structure.

• Additional strengths may include reinforcement learning, generative models, advanced ML techniques, simulation or optimization work, domain experience in finance or science-heavy industries, or contributions to open-source scientific software.