Tambourine
is one of the fastest-growing hospitality & tourism marketing firms.
Combining best-in-class tech with creative design, we revolutionize e-commerce for hotels, resorts and destinations.
Find us to learn more.
We are looking for a
Bilingual Data Engineer
to join our
Analitycs team in Bogotá.
The Senior Data Scientist is a strategic, hands-on leader who tackles our most challenging business problems with sophisticated machine learning and statistical techniques.
This role involves leading data science projects from ideation to production, mentoring junior team members, and acting as a trusted advisor to business stakeholders.
The ideal candidate combines deep technical expertise in advanced ML and MLOps with a pragmatic, business-oriented mindset to deliver high-impact models that shape company strategy.
This is an On-site position at our Bogotá Office
.
What we need from you:
- Bachelor's degree
- 5+ professional experience as Data Scientist
- Full
English proficiency
.
- Extensive experience with
advanced machine learning techniques
, such as NLP, deep learning, or complex time-series analysis.
- Expertise in designing, running, and analyzing
A/B tests
and a strong understanding of causal inference methods.
- Hands-on experience with
MLOps
, including deploying and maintaining ML models in a production environment (preferably on GCP/Vertex AI).
- Advanced skills in
Large-Scale Data Processing
using BigQuery and familiarity with tools like PySpark.
- Proven ability to
lead data science projects
independently from ideation to production.
- A passion for
mentoring
junior team members.
- Excellent
stakeholder management
skills, with the ability to act as a strategic advisor to the business.
- A pragmatic approach to problem-solving, with the ability to balance technical complexity with business needs and timelines.
Responsibilities
Advanced Modeling & Strategic Analysis:
- Develop and implement
advanced machine learning techniques
—such as NLP to analyze guest reviews, deep learning for recommendation engines, or sophisticated time-series analysis for demand forecasting.
- Devise novel strategies for leveraging complex datasets, such as
GA4 behavioral data
, to create predictive features for advanced personalization and dynamic pricing models.
- Lead research and development efforts to explore and validate new data sources and modeling approaches that can create a competitive advantage.
Experimentation & Causal Inference:
- Serve as the subject matter expert for
experimentation
, designing and analyzing complex A/B tests to rigorously measure the business impact of pricing strategies, marketing campaigns, and new product features.
- Employ causal inference methodologies to understand the true drivers of business outcomes in situations where traditional A/B testing is not feasible.
MLOps & Model Productionalization:
- Own the end-to-end lifecycle of machine learning models, including hands-on deployment, monitoring, and maintenance in a production environment using
Google Cloud Platform (GCP)
and tools like
Vertex AI
.
- Architect and implement robust data processing workflows for massive datasets, utilizing advanced skills in
BigQuery
and tools like
PySpark
when necessary.
Leadership & Stakeholder Management:
- Lead cross-functional data science projects
from initial conception and requirements gathering through to final deployment and impact measurement, managing timelines and deliverables.
- Mentor junior data scientists
, providing technical guidance, conducting constructive code reviews, and fostering a culture of innovation and continuous learning.
- Build strong relationships with business leaders, acting as a trusted advisor who proactively identifies and scopes opportunities where data science can create significant value.
Not required, but nice to have:
- A Master's degree or Ph.D. in a quantitative field (e.g., Statistics, Computer Science, Economics).
- Experience with reinforcement learning or other cutting-edge ML domains.
- Publications in peer-reviewed journals or presentations at major data science conferences.
- Experience building and scaling a data science function or team.