DATA SCIENCE STAFFING

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Costa Rica is a technology hub with an emerging opportunity in data science 

We partnered with a leader in nearshoring to realize this opportunity for data science staffing

Large talent pool of data science professionals

Conveniently located in the same timezone as the US

Most resources are available on-demand

Resources are bilingual in English and Spanish

Resources usually cost less than comp resources in US

It’s easy to request a data science resource. Just call or email us or spend a minute to fill out a form.  We’ll be in touch as soon!  

Please be sure to check out our data science profiles below

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DATA SCIENCE STAFFING

SOLUTIONS

Costa Rica is a technology hub with an emerging opportunity in data science 

L
L
L

We partnered with a leader in nearshoring to realize this opportunity

Large talent pool of data science professionals

Conveniently located in the same timezone as the US

Most resources are available on-demand

Resources are bilingual in English and Spanish

Resources usually cost less than comp resources in US

It’s easy to request a data science resource. Just call or email us or spend a minute to fill out a form.  We’ll be in touch as soon!  

Please be sure to check out our data science profiles below

Common questions about nearshoring

What is nearshoring?

Nearshore outsourcing is a practice where businesses hire services from companies in adjacent or nearby countries.  Our nearshoring partner is based out of Costa Rica.

Why nearshore data science?

  • Large pool of technical talent
  • Consultants are available on-demand
  • Bilingual in English and Spanish
  • Based out of Costa Rica on US timezone
  • Costa Rica shares many values with US
  • Lower cost than US-based outsourcing
  • Proven expertise in nearshoring

What is Costa Rica’s timezone?

Costa Rica is UTC -6

  • Mountain Standard Time
  • Central Daylight Savings Time

Who is newData’s partner?

novacomp is newData's nearshoring partner

A leader in nearshoring since 1987

What is newData’s role?

  • newData helps match the business’ job requirements with the right data science professionals offering a single point of contact
  • newData is a fully insured limited liability company that guarantees our clients’ satisfaction with all the solutions we offer

Why is Costa Rica a talent hub?

  • Stable politically and economically
  • Ranks high for technical education
  • English is commonly spoken
  • Home to hundreds of tech companies 
  • Flourishing medical technology
  • Draws talent from Central & South America

Data science profiles

MATH-BASED SCIENCES

The underpinning of data science is mathematics and statistics.  While most data science roles include some level of aptitude in math and stats, other roles require a graduate degree in one of these disciplines or PHD.  Other academic disciplines related to data science include Physics, Engineering, Economics, and Computer Science.

PROGRAMMING

Python, R, SQL, Scala, and Julia are just a few of the programming languages used by data scientists.  There are also programming languages like SAS and Stata that have been around long before today’s machine learning have come into play.  Programming languages like Java, C/C++, and PHP are used to develop software build around data science-related inputs and outputs

DATA VISUALIZATION

Data science output has many forms.  Since most people are visually oriented, it is no surprise that the visualization of data and the accompanying insights are in high demand.  The requirements range from displaying information / insights on a chart on a spreadsheet or presentation to developing interactive dashboards using business intelligence software like Tableau.

GENERAL DATA SCIENCE

Whether a business has limited data science resources or not, it usually makes sense to have a data science generalist on staff.  For larger businesses with an established data science function, a generalist may interact with business owners, programmers and other data science professionals.  For this reason, such a person will have a broad range of data science knowledge as well as business acumen but may not possess the deep skills that a data scientists with ML has.

DATA WRANGLING

The reality is that data scientists spend much of their time working with the data versus modeling.  For example, data hygiene, sourcing new data and integrating it with current data, and annotating data.  Skills to look out for include automation, developing workflows, APIs, and experience with multiple data formats.

STORY-TELLING

Information alone is not valuable.  It is what is done with that information that may be useful to a business.  A key skill is understanding a business, its competitors, consumers and the economic environment often reveal insights that can be made actionable.  However, unless the insights tell a story that is easy to follow, a business’ investment in data science is likely underutilized.  Such soft skills are different than the hard-core machine learning data scientists.

MACHINE LEARNING

ML algorithms play a large role in data science.  In some cases, they classified as supervised or unsupervised learning.  Common M techniques:

  • linear and logistic regression
  • naive Bayes classifiers
  • support vector machines
  • decision trees / clustering
  • tensorflow platform
  • ensemble learning with random forests and gradient boosting

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