Everything happens somewhere – which is why spatial analytics is fundamental to companies trying to understand the “where” and the “why” of their business. CARTO is the leading cloud-native location intelligence platform, trusted by data scientists, data analysts and developers from companies such as Vodafone, IKEA, Decathlon, Coca-Cola and Mastercard to provide geospatial insights for use cases such as site selection, geomarketing, route optimization, network planning and much more.

With an exceptionally diverse team of 150 people spread across the US and Europe, CARTO (backed by Insight Partners, Accel Partners, Salesforce Ventures, Earlybird Ventures, and Kibo Ventures, among others) is changing the way companies analyze location data – making it simple to do this straight out of modern, cloud data warehouses. Redefining its category, the company has grown rapidly in recent years providing a compelling alternative to legacy GIS software.

We are looking for an enthusiastic, experienced and self-motivated Data Scientist to work at the intersection of data science and GIS. In this role, you will be working in the Data Science team to develop new algorithms and derivative datasets that leverage the spatial nature of data to produce more accurate insights and predictions via CARTO’s Location Intelligence platform. You will also work with external clients to help solve their spatial data science problems and to bring new functionality to CARTO as a platform. There will be a strong focus on adapting existing machine learning methods to best work with spatial data and exploring new methods from the fields of spatial econometrics and machine learning.

The position is remote, preferably Central European Time zone. It’s also possible to work from our offices in Madrid or Seville.

You will
Build data science models, developing creative solutions to solve our customers’ more complex business problems (statistical and machine learning predictive models, with a focus on spatial models, multivariate analysis, hypothesis testing, probability theory and its applications, etc.).
Design and run novel data science experiments leveraging CARTO’s Data Observatory and the capabilities of the most advanced technologies to further develop our Analytics Toolbox.
Participate in all phases of the Data Science product lifecycle (exploratory data analysis, model development, model productionizing, rollout, and evaluation).
Lead research activities for new Spatial Data Science methods that drive activity on our platform, scale our business, and enhance the user experience.
Collaborate with engineering and product leaders, to frame and tackle a problem, both technically and within a business context.
Communicate rationale and findings from analyses to facilitate operational decisions for our clients. Present your work both to internal decision makers, and at conferences, meetups and leading workshops around spatial data science.
Keep up to date on new developments from industry and academia and presenting and educating the wider company at CARTO.




Ability to work with clients and other stakeholders to translate business problems into Data Science workflows.
Education in relevant fields and +3 years of experience working in Data Science.
Strong statistical background and experience applying data science techniques in production.
Strong foundation in coding skills relevant to Data Science; especially in Python and SQL. Spark will be a plus.
Experience working with geospatial data.
Experience working with Google Cloud, Snowflake, AWS and/or Azure ecosystems, and platforms like Google BigQuery, Databricks, AWS Redshift, etc.
Experience developing production-quality data products using the results of quantitative research.
Must be able to communicate effectively with (non-technical) senior executives internally and externally. Presentation skills are essential.
Fluency in English.

Bonus Points For
Experience with working with GIS software: CARTO, QGIS, ArcGIS, etc.
Experience working with clients in a professional services capacity.

Level of experience (years):

Mid Career (2+ years of experience)

Tagged as: , ,