Sr Data Scientist - Gcp

Coderio; Buenos Aires; Hybrid; ARS 136,828,000 - 228,047,000; Full time

Benefits offered by this job

Company-provided laptop

Job summary

ID90 Travel seeks a Data Scientist to bridge technical execution and business goals. You will own data science initiatives end-to-end, from problem framing to deploying reliable production models, partnering with data engineers and stakeholders to deliver measurable value for airline employee travel needs.

You will analyze complex travel datasets, design ML solutions, and turn data into practical recommendations with cross-functional teams in a hybrid setup.

Qualifications

• 3+ years in Data Science, Statistics, ML, or related field.
• Bachelor's/Master's/PhD in quantitative field or equivalent.
• Proven experience applying statistical and ML techniques to real problems.
• Experience owning data science projects from problem definition to production.
• Strong Python and data manipulation libraries.
• Strong SQL skills with relational databases & datasets.
• Solid statistics knowledge, model evaluation, experimentation, and data analysis.
• Ability to work with ambiguous problems and independently choose approaches.
• Ability to understand and evolve existing models and data science solutions.
• Strong communication skills for technical and business audiences.
• Experience working with containerized environments and production deployment.

Responsibilities

• Own data science projects end-to-end from problem understanding to deployment.
• Collaborate with stakeholders to define data problems and measurable goals.
• Choose appropriate techniques: statistics, ML, forecasting, or recommendations.
• Define success metrics and evaluate technical and business outcomes.
• Partner with Engineering to integrate data science into production systems.
• Explore complex datasets to uncover patterns and opportunities for decisions.
• Turn analyses into practical recommendations beyond raw findings.
• Design, develop, and validate models solving business problems.

Skills

Python

SQL

Pandas

ML

Education

Bachelor's in Statistics

Tools

Snowflake

BigQuery

Redshift

Job description

Type of work: Full time (M-F)- long term - contractor (USD) - Hybrid

Equipment: Company-provided work laptop

English Level: Advanced, written and spoken

The contractor must be based in: Córdoba, Argentina

About ID90 Travel

Did you know that one of the privileges of working for an airline is being able to fly around the world for free? At ID90 Travel, we provide the technology that makes airline employee travel possible for the world's leading airlines. We are the leading one-stop comprehensive solution for airline employee travel needs, offering exclusive rates on hotels, cruises, rental cars, and more. We are a team of travel industry insiders passionate about building modern, easy-to-use technology to solve complex challenges in travel. We are driven by continuous experimentation, data-backed ideas, and a shared passion for innovation. Here, you’ll find high autonomy and a supportive team environment where you are empowered to take initiative, learn from every iteration, and propose impactful solutions.

Job Description

Your mission as a Data Scientist at ID90 Travel is to bridge the gap between technical execution and business goals by owning data science initiatives end-to-end. You’ll be part of our Data Team, reporting to the Data Architect and working alongside Data Engineers, while partnering with stakeholders across the business. This is a highly cross-functional role where you’ll analyze complex travel datasets, design and validate machine learning solutions, and turn data into practical recommendations and reliable production models that drive measurable value for airline employee travel needs.

Key Responsibilities

• Own Data Science Projects End-to-End
• Own data science initiatives from understanding the business problem through exploration, solution design, development, validation, and deployment.
• Work with stakeholders to turn business questions and opportunities into well-defined data problems and measurable goals.
• Choose the right approach for each problem, whether that means statistical analysis, experimentation, machine learning, forecasting, recommendations, or a simpler solution.
• Define how success will be measured and evaluate solutions against both technical performance and business outcomes.
• Partner with Engineering and Infrastructure teams to integrate data science solutions into production systems.

Analyze Data and Generate Insights

• Explore and analyze complex datasets to uncover patterns, trends, and opportunities that can help guide business decisions.
• Work with teams across the company to understand their questions and help answer them with data.
• Identify assumptions, limitations, and potential biases in the data and communicate them clearly.
• Turn analysis into practical recommendations rather than simply reporting findings.

Build Data Science Solutions

• Design, develop, and validate models and algorithms that solve real business problems, such as customer segmentation, demand forecasting, recommendations, or other use cases.
• Use appropriate statistical and machine learning techniques while balancing accuracy, complexity, maintainability, and business value.
• Build solutions that can move beyond experimentation and be used reliably in real-world scenarios.
• Document important decisions, assumptions, dependencies, and limitations so solutions can be understood and evolved over time.

Own and Evolve Existing Initiatives

• Understand and take ownership of existing data science projects, including their business purpose, technical approach, assumptions, and data dependencies.
• Adapt and extend existing solutions as business needs and goals change.
• Evaluate whether an existing approach is still appropriate and recommend when it should be improved, simplified, redesigned, or replaced.
• Help ensure that existing data science initiatives continue to provide meaningful business value over time.

Collaborate and Communicate

• Work closely with Product, Engineering, Marketing, Operations, and other teams throughout the lifecycle of data science initiatives.
• Explain findings, recommendations, assumptions, and trade-offs clearly to both technical and non-technical audiences.
• Present complex ideas in a practical and understandable way.
• Help teams make better decisions by bringing data and evidence into the conversation.

Qualifications and Experience

Required

• 3+ years of experience in Data Science, Statistics, Machine Learning, Data Analytics, or a related quantitative field, gained through professional, academic, or a combination of both experiences.
• Bachelor's, Master's, or Ph.D. in Statistics, Mathematics, Computer Science, Engineering, Data Science, or another quantitative field, or equivalent practical experience.
• Proven experience applying statistical, analytical, and machine learning techniques to real-world business problems.
• Experience owning data science projects from problem definition through development, validation, and production deployment.
• Strong proficiency in Python and commonly used data science and data manipulation libraries.
• Strong SQL skills and experience working with relational databases and analytical datasets.
• Solid understanding of statistics, model evaluation, experimentation, and data analysis.
• Ability to work with ambiguous problems, ask the right questions, and independently determine an appropriate approach.
• Ability to understand existing models and data science solutions and evolve them as requirements change.
• Strong communication skills, with the ability to explain complex findings and trade-offs to both technical and business stakeholder audiences.
• Experience working with containerized environments and collaborating with Engineering teams to bring data science solutions into production.

Desired

• Previous experience in e-commerce, marketplaces, travel, recommendations, forecasting, or customer analytics is a plus.

Required Skills/Abilities

• Autonomy & Navigating Ambiguity
• Business-Value Orientation & Critical Thinking
• Effective & Adaptive Communication
• Cross-Functional Collaboration
• Ownership & Accountability
• Initiative & Continuous Learning Mindset

Why Join Us

• Travel Benefits: Exclusive insider rates and deals on hotels, cruises, rental cars, and global travel products through the ID90 Travel platform.
• Competitive Compensation: Competitive remuneration package in USD
• Hybrid Environment: A work setup offering hybrid flexibility to manage your work-life balance.
• Global Product Impact: The opportunity to build technologies that directly empower airline employees and transform global travel logistics.

Prometeo Talent – Data Analyst (Machine Learning)

What do we offer? Prometeo Talent is a recruitment agency with a strong presence across the Americas and Europe. We specialize in connecting companies with exceptional professionals across data, analytics, and business functions.

We have partnered with a U.S.-based company specialized in marketing effectiveness measurement. They help organizations better understand consumer behavior, purchasing trends, brand performance, and market dynamics through data-driven insights.

We are looking for a Data Analyst (Machine Learning) to join our client's global team.

This role is ideal for analytical and data-driven professionals who enjoy working with large-scale consumer and retail datasets, bridging the gap between complex database structures and business strategy, and turning raw transactional or behavioral data into clear, actionable metrics.

The ideal candidate combines solid SQL and Python skills, a strong "numbers sense", attention to detail, and the ability to translate technical results into clear summaries for stakeholders in English. The position is open to a range of experience levels, from recent graduates with a strong quantitative background to mid-level analysts looking to deepen their technical data manipulation skills.

What You Will Do

• Write, optimize, and execute SQL queries to extract data from relational databases and cloud data warehouses.
• Develop, run, and validate machine learning, predictive, or statistical matching models to uncover deeper consumer behaviors and forecast trends.
• Slices, unpivots, and aggregates raw transactional or panel data to calculate core business metrics such as household penetration, market share, and purchasing trends.
• Build and maintain clean, structured data tables and views that feed into recurring tracking reports and stakeholder dashboards.
• Review query outputs, perform data audits, and double-check logic to ensure accuracy and data integrity before insights are finalized.
• Use conditional logic and filtering techniques to segment data and isolate specific tracking cohorts.
• Clean, parse, and categorize unstructured or semi-structured text data.
• Translate technical data structures and query results into clear written summaries, bullet points, or data tables for stakeholders in English-speaking environments.

Requirements

• 0 to 4 years of experience in Data Analysis, Business Analytics, or similar roles. Entry-level candidates with strong relevant coursework, personal data projects, or internships are welcome, as well as mid-level professionals with a proven track record of querying databases to drive business insights.
• Advanced English level (written and spoken).
• Foundational to intermediate SQL skills, including complex JOIN logic, standard aggregations, and CASE WHEN conditional logic.
• Python scripting for data cleaning, manipulation, and processing, with proficiency in pandas for complex multi-step transformations.
• Familiarity with modeling libraries such as scikit-learn or statsmodels to build and evaluate predictive or statistical matching models.
• Experience working with messy text fields: cleaning, parsing, filtering, and pattern matching within string variables.
• Solid understanding of relational database design, including how tables connect, primary/foreign keys, and data normalization.
• Bachelor's degree in Business Analytics, Statistics, Mathematics, Computer Science, Economics, Marketing, or a related field with coursework covering database management or quantitative data analysis.

Nice to Have

• Experience working with consumer, retail, panel, or transactional datasets.
• Familiarity with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift).
• Exposure to marketing analytics, consumer insights, or market measurement environments.
• Experience building data tables or views that feed dashboards and recurring reports.