We Help You Hire a Data Scientist From South America
Your next project can often be stressful and time-consuming if you cannot find the right nearshore company to hire with. Smart, responsible, hardworking, and experienced Data Scientists are what you need for your role.
The AI matching algorithm that we use helps your organization find the ideal data scientist for your project. The method we use to match your team is a combination of data and personal interviews.
We are dedicated to helping you hire Data Scientists who will contribute quality work right from the start. This is why we have refined our hiring processes over the past several years and have become a trusted partner for many startups that are growing rapidly. Add an expert to your team with Hire SQL and hire your future Data Scientist.
Work to US Time
Our data scientists work US hours and are happy to communicate with your existing team so they can gel with them.
Brazilian Rates
Our team is made up of talented and experienced data scientists from Brazil who are eager to work with US companies.
No Employment Tax
Benefits, local employment taxes, and other employment-related expenses are handled by our company.
Tested Data Scientists
It ensures your hiring a skilled professional who has passed our testing process when you hire a software engineer from us.
Testimonials
What is Data Science?
Data science is an interdisciplinary field that combines scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It encompasses various techniques, tools, and processes to uncover patterns, make predictions, and drive informed decision-making.
Data Collection and Preparation
Data science involves the collection and aggregation of vast amounts of data from diverse sources. This includes identifying relevant data sources, extracting data, cleaning and preprocessing it, and transforming it into a suitable format for analysis. This stage ensures that the data is accurate, complete, and ready for analysis.
Data Exploration and Analysis
Once the data is prepared, data scientists employ statistical techniques, machine learning algorithms, and data visualization tools to explore and analyze the data. They uncover patterns, trends, and relationships within the data, identify key variables, and gain insights into the underlying processes and phenomena.
Data Modeling and Prediction
Data scientists develop models and algorithms to predict future outcomes, make forecasts, and classify or cluster data. They leverage techniques such as regression analysis, decision trees, neural networks, and natural language processing to build predictive models that can generate valuable insights and assist in making data-driven decisions.
Extracting Insights and Knowledge
To summarize, data science is the practice of extracting insights and knowledge from data through data collection, exploration, analysis, and modeling. It enables organizations to leverage data as a strategic asset, uncover actionable insights, and make informed decisions to drive innovation and success.
How does Data Science help Businesses?
Data science has emerged as a transformative force for businesses, enabling them to harness the power of data for strategic decision-making and achieving a competitive edge in today’s data-driven landscape. Here are three key ways in which data science helps businesses:
Data-Driven Decision-Making
Data science empowers businesses to make informed decisions based on accurate insights derived from data analysis. By leveraging advanced analytical techniques and machine learning algorithms, businesses can uncover hidden patterns, trends, and correlations in their data. These insights provide a solid foundation for strategic planning, risk assessment, and identifying growth opportunities. Data-driven decision-making minimizes guesswork, reduces risks, and enhances the overall success rate of business strategies.
Enhanced Operational Efficiency
Data science plays a vital role in optimizing business operations and resource allocation. By analyzing operational data, businesses can identify inefficiencies, bottlenecks, and areas for improvement. Predictive analytics enables accurate forecasting, helping businesses optimize inventory levels, manage supply chains, and streamline production processes. Furthermore, data-driven insights support proactive maintenance, reducing downtime and maximizing operational efficiency. By leveraging data science, businesses can enhance productivity, reduce costs, and improve overall operational performance.
Improved Customer Experience and Personalization
Data science enables businesses to understand their customers at a deeper level and deliver personalized experiences. By analyzing customer data, businesses can segment their customer base, identify behavioral patterns, and tailor their marketing efforts accordingly. Personalized recommendations, targeted marketing campaigns, and customized offerings enhance customer satisfaction, drive customer loyalty, and increase sales. Data science also enables sentiment analysis and social media monitoring, providing valuable insights into customer preferences, feedback, and sentiment, allowing businesses to adapt their strategies and offerings to meet customer expectations.
Data Science Empowers Businesses
In summary, data science empowers businesses with data-driven decision-making, enhanced operational efficiency, and improved customer experience. By leveraging the power of data, businesses can gain a competitive advantage, drive innovation, and achieve long-term success in today’s data-centric business landscape.
- Gain Competitive Advantage
- Data Driven Decision Making
- Improve Efficacy
- Identify Waste and Overspending
- Predict Demand
- Customer Experience Monitoring
- Cash Flow Insights
- Better Business Strategies based on Data Insights
Igor Lopes
Data Scientist
11+ Years of Data Science 11+ Years of SQL, He has a wealth of data science expertise across many industries
Gustavo Batista
DATA Scientist
8+ Years of Data Science 8+ Years of SQL. He enjoys creating useful business insights for stakeholders
Edvaldo Fernandes
Sr Data Scientist
13+ Years of Data Science 10+ Years of SQL . Experienced senior data scientist who enjoys a challenge.
What does a Data Scientist do?
A data scientist is a key player in the field of data science, responsible for extracting insights and knowledge from complex datasets. They employ a combination of statistical analysis, machine learning algorithms, and programming skills to solve complex business problems. Here are two primary areas of focus for data scientists:
Data Analysis and Modeling
Data scientists explore and analyze large datasets to uncover patterns, trends, and correlations. They apply statistical techniques and machine learning algorithms to extract valuable insights and develop predictive models. By identifying relevant variables and understanding their impact, data scientists help businesses make data-driven decisions and forecast future outcomes. They also evaluate the performance of models, fine-tune them, and ensure their accuracy and reliability.
Data Preparation and Feature Engineering
A significant portion of a data scientist’s role involves data preparation and feature engineering. They clean and preprocess raw data, handle missing values, and transform data into a suitable format for analysis. Feature engineering involves selecting and creating relevant variables that contribute to accurate predictions and insights. Data scientists also work with databases, perform data integration, and ensure data quality and integrity.
Deriving Insights, and Developing Predictive Models
Data scientists play a crucial role in analyzing and modeling complex datasets, deriving insights, and developing predictive models. They possess a blend of statistical, programming, and domain expertise, enabling them to effectively extract valuable knowledge from data and contribute to data-driven decision-making processes.
We Are A Solid Data Science Partner
When using your data to derive insights and drive business decisions, Hire SQL understands the importance of working with a reliable and expert partner. As a result, we provide a premium and professional service to all our clients. Get in touch with us now to start your data science team!
How do you Hire a Data Scientist with us?
Follow these three steps to hire a Data Scientist:
Please click on the button below and click send on the form once you’ve written a description of your project and needs.
I interview Data Scientists based on their resumes. Start shortlisting professionals you want to interview once proposals start coming in.
When you write your requirement description, you determine the scope of your work and the type of Data Scientist you need.
In order to receive a fast and detailed response, please include the following information:
Detailed deliverables: From e-commerce to ERP and big data analytics, list them all.
Identify whether the project is small or large in your job posting.
Let us know if you prefer experience with certain industries.
Billing: Please indicate your preference for hourly rates versus priced monthly contracts.
How much does it cost to Hire a Data Scientist?
Several factors affect cost, including expertise, experience, market conditions, and location.
Additionally, an experienced Data Scientist will provide higher-quality results, work faster, and have more specialized knowledge.
Less experienced data scientists may price their services lower but are unlikely to produce similar output to more experienced data scientsists.
Below are the rates for hiring our South American Data Scientist:
Junior
Prices From- Works to U.S time zones
- No Recruitment Fees
- Vetted Skills & Experience
- Fulltime Working for you
- No Unreliable Freelancers
Intermediate
Prices From- Works to U.S time zones
- No Recruitment Fees
- Vetted Skills & Experience
- Fulltime Working for you
- No Unreliable Freelancers
Senior
Prices From- Works to U.S time zones
- No Recruitment Fees
- Vetted Skills & Experience
- Fulltime Working for you
- No Unreliable Freelancers
We have Data Scientists in South America available for hire!
Depending on the exact skill requirements and experience requirements, the price of data scientists may vary slightly.
Depending on your project, you’ll need to determine which is right for you.
What kind of SQL Queries does a Data Scientist write?
Data scientists play a crucial role in extracting valuable insights from data to meet business needs and drive informed decision-making. One of their key responsibilities is writing queries to analyze and retrieve specific data sets for analysis. These queries are designed to uncover patterns, relationships, and trends that provide valuable business insights. Here’s an example of an SQL query a data scientist might use to calculate the percentage of sales generated in Florida from all North American sales figures:
SELECT (SUM(CASE WHEN state = 'Florida' THEN sales_amount ELSE 0 END)
/ SUM(CASE WHEN region = 'North America' THEN sales_amount ELSE 0 END))
* 100 AS percentage_sales
FROM orders
WHERE region = 'North America';In this example, the data scientist is querying an “orders” table to calculate the percentage of sales generated in the state of Florida from all North American sales figures. The query uses conditional statements to filter the data based on the state and region, and then calculates the percentage by dividing the sum of sales in Florida by the sum of sales in North America, multiplied by 100.
By leveraging SQL queries and similar techniques, data scientists can analyze large datasets and extract specific information needed to gain valuable insights into business performance, customer behavior, and other important factors that drive decision-making.
Interview Questions to ask a Data Scientist before you hire them
Can you walk me through your experience with applying machine learning algorithms to real-world problems?
This question allows the candidate to showcase their practical experience and demonstrates their ability to translate theoretical knowledge into actionable solutions. It provides insights into their expertise in applying machine learning techniques and their familiarity with common challenges faced in real-world scenarios.
How do you evaluate the performance of a machine learning model? Can you explain different evaluation metrics you have used?
This question assesses the candidate’s understanding of model evaluation techniques and their ability to select appropriate metrics based on the problem at hand. It reveals their proficiency in assessing model accuracy, precision, recall, and other relevant performance indicators, showcasing their ability to make informed decisions based on evaluation results.
Can you describe a time when you faced a significant data quality issue and how you addressed it?
This question reveals the candidate’s problem-solving skills and their ability to handle data quality challenges, which are common in real-world data science projects. It demonstrates their resourcefulness in identifying and resolving data issues, ensuring the reliability and integrity of the analysis performed.
Do you enjoy Working Alone or within a Team?
There is no right or wrong answer to this question since A Data Scientist can have any combination of skills. Having a data scientist who is hardworking and independent may be important to you. You might prefer someone who works well with others and isn’t stubborn.
How do you stay updated with the latest advancements and trends in the field of data science?
This question highlights the candidate’s enthusiasm for continuous learning and their commitment to staying abreast of evolving industry practices. It reveals their engagement with the data science community, their awareness of emerging technologies, and their ability to leverage new tools and techniques to enhance their work.
A Brief History of Data Science
Data science is a multidisciplinary field that combines statistical analysis, machine learning, and domain expertise to extract insights and knowledge from data. Here’s a brief history of data science:
Emergence and Early Development
Data science traces its roots back to the 1960s and 1970s when statisticians began exploring methods to analyze and interpret large datasets. The emphasis was on descriptive and inferential statistics, primarily in academic and research settings.
Technological Advancements and Big Data Era
In the late 1990s and early 2000s, advancements in computing power and the proliferation of digital data sources led to the emergence of data science as a distinct discipline. The term “data science” gained popularity, and with the advent of big data, there was a shift towards applying machine learning and predictive analytics techniques to extract insights from massive datasets.
Mainstream Adoption and Industry Impact
In recent years, data science has become increasingly mainstream across industries. Organizations recognize the value of data-driven decision-making and invest in building data science teams. The field has witnessed rapid advancements in algorithms, tools, and technologies, enabling more sophisticated analysis, such as deep learning and natural language processing. Data science is now integral to various domains, including finance, healthcare, marketing, and e-commerce, revolutionizing how businesses operate and drive innovation.
From Statistical Analysis to Encompass Advanced Machine Learning
In summary, data science has evolved from statistical analysis to encompass advanced machine learning techniques and has transformed into a vital discipline with widespread industry applications. The field continues to evolve, driven by technological advancements and the growing importance of data-driven decision-making in today’s data-rich world.
Why you should choose us to hire a Data Scientist?
As a leading Nearshore Technology Solutions provider, we provide experienced data scientists at reasonable prices. High-performance, scalable solutions are our goal for our clients.
Our data science skills and data project management experience are utilized to accelerate or rescue data projects. Throughout the planning phase and beyond, we strive to create long-term value.
Since 2014, we’ve matched skillful data scientists with great teams for over a hundred startups and tech companies worldwide.
You will find our data scientists to be devoted members of your team, fully integrating into your team’s operation.
Data Scientists that we hire undergo a thorough vetting process to ensure they have the necessary communication skills, remote work readiness, and technical skills (both for Data Science knowledge depth, as well as for a wide range of computer science and database knowledge).
Reduce Costs
In order to reduce costs, companies usually outsource. The cost of hiring data scientists outside the United States is lower. Additionally, you will be able to reduce overall employment costs significantly. There won’t be any US or European employment taxes, benefits, redundancy liabilities, or office space fees.
Ramp Up Faster
Taking advantage of growth and downsizing quickly can be a great competitive advantage for any industry. When you hire Brazilian data scientists through us, you will be able to scale up or down as needed, and do so relatively easily.
Trusted Partner
By outsourcing data science, you’re entrusting your project to a company with extensive experience helping businesses succeed. Thus, you can rest assured that your application will be delivered on schedule and within budget.
What kind of tasks do Businesses ask Data Scientists to perform?
Businesses rely on data scientists to perform a wide range of tasks to derive actionable insights and drive data-driven decision-making. Some common tasks include data collection and preprocessing, exploratory data analysis, developing predictive models, conducting statistical analysis, and designing experiments.
Data scientists are often tasked with identifying relevant variables, feature engineering, and applying machine learning algorithms to develop accurate predictive models. They are responsible for evaluating model performance, fine-tuning models, and making recommendations based on the analysis.
Businesses also rely on data scientists to communicate findings and insights effectively to non-technical stakeholders. Additionally, data scientists may be involved in data visualization, creating interactive dashboards, and automating data-related processes.
Overall, businesses seek data scientists to leverage their expertise in extracting knowledge from data to solve complex problems, optimize processes, and drive strategic decision-making.
- Gain Competitive Advantage
- Data Driven Decision Making
- Improve Efficacy
- Identify Waste and Overspending
- Predict Demand
- Customer Experience Monitoring
- Cash Flow Insights
- Better Business Strategies based on Data Insights
How do we test a Data Scientist to check his skills?
Testing the skills of a data scientist requires a comprehensive evaluation to assess their proficiency in key areas. One effective approach is through a combination of technical assessments, case studies, and interviews.
Technical assessments may involve coding challenges in languages like Python or R, where candidates are evaluated on their ability to manipulate data, apply statistical analysis, and build machine learning models. Case studies provide candidates with real-world scenarios to showcase their problem-solving skills, hypothesis formulation, and interpretation of results.
Interviews allow for a deeper exploration of their knowledge, experience, and thought processes. Additionally, evaluating their communication skills is essential, as data scientists should be able to effectively convey complex concepts and insights to both technical and non-technical stakeholders.
Assessing their domain expertise, familiarity with relevant tools and techniques, and their ability to work with large datasets are also critical aspects to consider.
How do you integrate a Data Scientist into your existing team?
Integrating a data scientist into your existing team is a crucial step towards leveraging their expertise and maximizing their impact. A smooth integration process ensures collaboration, alignment, and effective utilization of their skills. Here are four key steps to integrate a data scientist into your existing team:
Clearly Define Roles and Responsibilities
Clearly outline the roles and responsibilities of the data scientist within the team. Identify the specific areas where their skills and expertise will be utilized, such as data analysis, model development, or strategic decision-making. This clarity helps establish expectations and facilitates seamless collaboration.
Foster Communication and Collaboration
Promote open lines of communication and collaboration between the data scientist and other team members. Encourage regular meetings, brainstorming sessions, and knowledge sharing to facilitate cross-functional collaboration and foster a collaborative team culture.
Provide Access to Relevant Data and Tools
Ensure the data scientist has access to the necessary data sources and tools required for their work. Collaborate with IT and other stakeholders to provide access to relevant datasets, data infrastructure, and software tools. This enables them to perform their analyses effectively and efficiently.
Encourage Continuous Learning and Professional Growth
Support the data scientist’s professional development by providing opportunities for learning and growth. Encourage participation in relevant conferences, workshops, and training programs. Additionally, promote knowledge sharing within the team, enabling the data scientist to learn from their peers and contribute their expertise to others.
By following these steps, organizations can integrate data scientists into existing teams successfully, fostering collaboration, maximizing their potential contributions, and driving data-driven decision-making across the organization.
How long on average does a Data Scientist stay at a company?
Data Scientists in the US tend to stay between 1.8 and 3.2 years in their jobs, with larger companies keeping workers longer. On average, Brazilians spend between 2.4 and 4.1 years in the job, with Sao Paulo residents spending the shortest amount of time there. Previously, people tended to spend their entire careers with the same company.
Data Scientsists with experience may depart from one job to another for new opportunities and more money, as such longevity is no longer the norm. The most in-demand data science pros can migrate between jobs (or even freelance) fairly easily in this market due to a low unemployment rate and a great need for professionals with SQL, cloud databases, and other important skills. We offer perks to attract and retain top talent, and we value communication with employees.
Frequently Asked Questions (FAQs)
Our services are trusted by hundreds of startups and tech companies worldwide, and we have matched hundreds of skilled data scientists to great teams in the US, UK and Canada. Every data scientist in our network goes through a vetting process to verify their communication abilities, remote work readiness, and technical skills, both for depth in data science and SQL and across the greater data domain.
The job description of a Data Scientist should include the following:
Developing, implementing, and managing data insights
New database testing and evaluation
Enhancing existing data by identifying areas for improvement
Writing queries in an efficient manner
Analyzing operational opportunities for improvement
Establishing procedures for quality assurance
Implementing data tools, processes, and metrics
Upgrades and maintenance of existing data stores
Assisting other developers, UX designers, and business analysts with their tasks
It’s not enough to just ship features; your data needs to help your business succeed. In order to better understand what you’re building, for whom, and why, we’ll begin our collaboration with a discovery process.
Our headquarters are in Sao Paulo, Brazil. We have clients from all over the world. We have successfully collaborated with companies in North America, Asia, the Middle East, and Europe. A good understanding of each client and excellent English communication skills help the process run smoothly.
We can work with you to scale the team down as needed and make sure you have the correct skills required for each project phase.
All Types! You can hire a data scientist on a full-time, part-time, or contract-to-hire basis at Hire SQL. You can find a data scientist in a time zone that suits your needs thanks to our global network of skilled data engineers. Data Scientists who work remotely for us are all mid- and senior-level professionals, ready to work right away.
