Senior Data Scientist | Databricks | AI | Spark | Mexico

At TeamStation AI, we are on a mission to bring together the brightest minds to solve tomorrow’s toughest technology challenges. Our work is about more than just AI—it’s about building the future through collaboration and innovation. We believe that the key to solving the world’s most complex problems lies in aligning diverse talents and perspectives. Our AI-powered platform enables cutting-edge scientific and technical teams to work smarter, faster, and together. By joining us, you’ll help unlock new technological breakthroughs and drive innovation where it matters most.

Join the Mission at TeamStation AI!

Where do we come from? We are seeking visionaries, innovators, and problem solvers who thrive in fast-paced, collaborative environments. If you’re passionate about AI, technology, and solving critical challenges, we want to hear from you. Come be part of a team where your ideas can drive the future.

Location: Remote — Mexico or Brazil
Industry: Media / Out-of-Home Advertising

Role Overview

We are looking for a Senior Data Scientist who can turn large, messy datasets into trusted methods, clear decisions, and useful data products.

This is an applied Data Science role. It is not a pure research role or a pure Software Engineering role. Most of your time will go into methodology development, data cleaning, validation, and working with Engineering to make solutions useful at scale.

Research is a small part of the role. Engineering is important, but deeper production skills can be developed during onboarding.

You will work closely with Product Managers, Data Engineers, Software Engineers, and business teams.

How You Will Spend Your Time

40% — Methodology Development

• Build measurement and attribution methods.
• Design approaches for incremental lift, experiments, and causal analysis.
• Turn business questions into clear metrics, assumptions, and decision rules.
• Compare methods and explain why one approach fits the problem better.
• Document methods so Product, Engineering, and business teams can use them.
• Explain uncertainty, limits, and tradeoffs in simple language.

25% — Data Cleaning and Validation

• Work with large, messy datasets from multiple sources.
• Find missing values, incorrect records, conflicting definitions, and quality problems.
• Clean, transform, join, and validate data before analysis begins.
• Review data lineage, features, transformations, and business rules.
• Build repeatable checks that turn raw data into trusted data.
• Explain when data is not strong enough to support a conclusion.

20% — Engineering

• Write clean and maintainable Python, SQL, and Spark code.
• Build and improve workflows in Databricks.
• Support ETL, feature preparation, models, and attribution dashboards.
• Work with engineers on testing, deployment, performance, and reliability.
• Help move methods and models into practical data products.

Deep Software Engineering or MLOps experience is helpful, but it is not a hard requirement. Strong Data Scientists who can grow on the engineering side are welcome.

10% — Research

• Review new methods, tools, and technical approaches for specific business problems.
• Test whether a new approach improves accuracy, speed, or decision quality.
• Turn useful research into methods the team can apply.

This is not an academic or research-heavy role.

5% — Other Work

• Join planning, Product, and stakeholder discussions.
• Support documentation and knowledge sharing.
• Help review work and guide less experienced team members.
• Contribute to roadmap and team priorities.

What You Will Own

• Reliable methods that answer real product and business questions.
• Trusted datasets ready for analysis, modeling, and reporting.
• Measurement frameworks for attribution and campaign effectiveness.
• Practical models and data products that can work at scale.
• Clear documentation covering assumptions, decisions, limits, and results.
• Clear communication with technical and non-technical teams.

Required Experience

• 5+ years of professional Data Science or related applied-data experience.
• Strong hands-on experience with Databricks and Apache Spark.
• Advanced Python and SQL skills.
• Experience with pandas, NumPy, scikit-learn, or similar tools.
• Strong knowledge of statistics, experimentation, and causal inference.
• Experience with A/B tests, switchback tests, quasi-experiments, or related methods.
• Proven experience cleaning and validating large, messy, multi-source datasets.
• Experience building predictive models or data products used by real teams.
• Ability to turn unclear business questions into structured analytical work.
• Experience working with Product Managers, Data Engineers, and Software Engineers.
• Ability to explain technical decisions and uncertainty in clear, concise English.
• Experience with Git and cloud data systems such as AWS.
• Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, or a related field.

Preferred Experience

• Measurement, attribution, incremental lift, or advertising-data experience.
• Production deployment, monitoring, MLOps, or CI/CD.
• Building or maintaining scalable ETL and feature pipelines.
• Geospatial or location-based data experience.
• GeoPandas, Shapely, PostGIS, Census, or demographic datasets.
• Media, AdTech, or Out-of-Home advertising experience.
• Mentoring other Data Scientists or reviewing technical work.

Who This Role Fits

This role fits someone who:

• Enjoys building methodology and solving difficult data problems.
• Is comfortable spending real time cleaning and validating data.
• Wants Data Science work tied to products and business decisions.
• Can balance scientific rigor with practical delivery.
• Likes working across Data Science, Product, and Engineering.
• Can explain complex work in a short and clear way.

This role may not fit someone who:

• Wants most of their time focused on academic research.
• Wants to build models without cleaning or validating data.
• Wants a pure Software Engineering, Platform Engineering, or MLOps role.
• Prefers analysis that never moves into products or business workflows.

What We Will Evaluate

Candidates should be ready to explain one real project they personally owned:

• The business or product problem.
• The raw data and quality problems.
• The cleaning and validation work.
• The methodology selected and why.
• The code and engineering work personally completed.
• The final result, including measurable impact when available.

A public GitHub profile is welcome but not required. A private walkthrough of relevant work also works. Never share confidential employer code or data.

Work Environment

• Fully remote work.
• Cross-functional Product, Data Science, and Engineering team.
• Real advertising measurement problems at scale.
• Support to grow production-engineering skills.

Learn more at TeamStation AI.