We Help You Hire an AI Data Scientist From South America
You can often find it difficult and time-consuming to find an ideal nearshore company to handle your upcoming AI data science project. It is important to find a AI Data Scientist who is smart, experienced, hardworking, and has proven experience with AI and data science skills.
To find the optimal development partner for your project, we use a custom AI matching algorithm, to find you the perfect data scientist from our database. For an excellent match to your needs, we combine data and personal interviews.
Hire an AI Data Scientist who will contribute quality work to your team from day 1. Our hiring process has been refined over several years, and it’s trusted by many fast-growing startups. Add an expert AI data scientist to your team by hiring from us.
South American AI Data Scientists
Our team is made up of talented and experienced ai data scientists from Brazil who are eager to work with US companies.
South American Rates
We have gathered together a great group of talented and experienced ai data scientists from South America who are eager to work with US companies.
Save With One Low Cost Fee
Our company takes care of all employee benefits, local employment taxes, and other costs related to employment..
Tested Data Scientists Only
Choosing an AI data scientist from us guarantees you’re hiring a skilled professional who’s been tested in both AI skills and data science.
Testimonials
Why Hire an AI data Scientist?
As we move into an era where data is considered the new oil, organizations are racing to harness its power to drive business decisions, improve customer experiences, and create a competitive edge. For software, web, and financial companies, these dynamics are particularly pronounced. These sectors deal with massive amounts of data daily, presenting immense opportunities for AI-driven insights. Consequently, the role of an AI data scientist becomes increasingly critical, as they hold the key to transforming raw data into actionable intelligence. Here are three key benefits these companies can reap by hiring an AI data scientist.
Enhancing Decision-Making with Predictive Analysis
AI data scientists are experts in predictive analytics, a realm that goes beyond simple data analysis. They use machine learning and AI algorithms to forecast future events based on historical data. For software, web, and financial companies, predictive analytics can help foresee user behavior, market trends, and potential risks, thereby enabling more informed and strategic decision-making.
Streamlining Operations Through Automation
AI data scientists can develop models that automate routine tasks, significantly reducing the time and effort required for these processes. For instance, in software and web companies, they can automate the process of identifying bugs or monitoring user interactions. In financial companies, they can create models for automated trading or fraud detection. This automation not only improves efficiency but also allows the workforce to focus on more critical, high-level tasks.
Driving Business Innovation
The capabilities of an AI data scientist can also lead to innovative solutions and products. By exploring and experimenting with data, they can uncover insights that could lead to the development of new features, services, or even entire business models. For example, in a web company, data scientists could discover new ways to personalize user experiences, while in a financial company, they could devise new strategies for portfolio management.
In conclusion, an AI data scientist is an invaluable asset to software, web, and financial companies. Their ability to analyze, interpret, and predict using data can lead to improved decision-making, streamlined operations, and innovative solutions, giving these companies a significant edge in today’s data-driven landscape.
Why Use AI For Data Science?
The intersection of AI and data science has revolutionized how businesses approach data analysis and decision-making. AI brings automation, scalability, and advanced analytics capabilities to the field of data science, enabling businesses to extract valuable insights from their data faster and more efficiently. In this context, leveraging AI for data science offers several key advantages that can propel businesses forward. Let’s explore three major benefits of using AI for data science.
Accelerating Data Processing and Analysis
Data science involves dealing with massive volumes of data that can be time-consuming and resource-intensive to process and analyze manually. AI techniques, such as machine learning and deep learning, can automate and expedite these tasks. AI algorithms can quickly sift through vast datasets, identify patterns, and extract relevant information, enabling businesses to analyze and derive insights from their data at an unprecedented speed. This accelerated data processing and analysis translate into faster decision-making and the ability to respond promptly to market trends and customer demands.
Improving Accuracy and Predictive Capabilities
AI algorithms are designed to learn from data patterns and make accurate predictions or classifications. By employing AI in data science, businesses can enhance the accuracy and reliability of their models and predictions. AI techniques enable data scientists to develop sophisticated predictive models that can uncover hidden patterns and trends in data, enabling businesses to anticipate customer behavior, identify market opportunities, and mitigate risks. This predictive power empowers businesses to make data-driven decisions with a higher level of confidence and precision.
Enabling Personalization and Enhanced Customer Experiences
AI-powered data science enables businesses to gain a deeper understanding of their customers’ preferences, behaviors, and needs. By leveraging AI techniques like natural language processing (NLP) and recommendation systems, businesses can personalize their products, services, and marketing efforts. AI algorithms can analyze customer interactions, social media data, and other sources of information to create personalized recommendations, tailored advertisements, and customized user experiences. This level of personalization not only enhances customer satisfaction but also drives customer loyalty and revenue growth.
Uncovering Hidden Insights and Business Opportunities
AI can uncover valuable insights and business opportunities that might have gone unnoticed through traditional data analysis methods. AI algorithms excel at detecting complex patterns, correlations, and anomalies in data. By applying AI techniques to diverse datasets, businesses can uncover hidden relationships, identify emerging trends, and discover new market segments or niches. These insights can drive innovation, inform product development, and enable businesses to gain a competitive advantage in the market.
- Faster and more accurate data analysis.
- Improved decision-making based on data insights.
- Enhanced predictive capabilities for future trends.
- Efficient automation of routine data tasks.
- Personalized customer experiences and recommendations.
- Uncovering hidden patterns and business opportunities.
- Competitive edge through innovative data-driven solutions.
- Scalable data processing for business growth.
Pedro Barbosa
AI Data Scientist
11+ Years of Data Science 4 Years of using Data Science. with AI
Ricardo Vieira
AI Data Scientist
4+ Years of AI Data Analysis 9+ Years of Data Science.
João Souza
AI Data Engineer
5+ Years of AI 10+ Years of Data Science.
What does a AI Data Scientist do?
An AI Data Scientist plays a crucial role in leveraging the power of artificial intelligence and data science to extract meaningful insights from vast amounts of data. Their primary responsibility is to analyze, interpret, and derive valuable information from data using advanced statistical and machine learning techniques. They work closely with domain experts and stakeholders to understand the business objectives and identify relevant data sources.
An AI Data Scientist designs and develops predictive models and algorithms to uncover patterns, trends, and correlations in data. They preprocess and clean the data, select appropriate features, and apply suitable machine learning algorithms to train models. They validate and fine-tune these models to ensure their accuracy and reliability in making predictions or classifications.
Additionally, an AI Data Scientist evaluates and tests different AI technologies and tools to identify the most effective ones for specific business applications. They stay updated with the latest advancements in AI and data science and continuously enhance their skills and knowledge to keep pace with the rapidly evolving field.
An AI Data Scientist combines expertise in mathematics, statistics, programming, and domain knowledge to unlock the full potential of data and enable data-driven decision-making, automation, and innovation within the organization.
An AI Data Science Partner You Can Trust
A high-performing, experienced and reliable partner is important when doing data science with AI. Because of this, we offer a premium and professional service to all of our clients. Let’s get started on your AI enhanced data science team right now!
How to Hire a AI Data Scientist?
Here is how to hire an AI Data Scientist with us, if you’d like a detailed, fast response, please include the following:
Outline all the deliverables you require, from database reports to using data APIs to big data analytics.
The project size should be indicated in your job message.
Here you can mention your experience with certain industries, such as finance.
Your preference for hourly rates vs. prices per month should be noted.
- Scope of work: From data sources, reports and the problems you wish to solve or predict, list all the deliverables you’ll need.
- Project length: Your job post should indicate whether this is a smaller or larger project.
- Background: If you prefer experience with certain types of AI or previous industries
- Billing: Note your preference for hourly rates vs. priced monthly contracts.
How much does it cost to Hire an AI Data Scientist?
A variety of factors influence costs, such as expertise, experience, location, and market conditions.
An AI Data Scientist with lots of experience won’t just command higher fees, but can also deliver higher-quality work, work faster, and have a wider range of skills and more experience with AI tools.
As they gain experience, Junior data scientists will price their AI Data Science services lower.
For South American AI Data Scientists, our hourly rates are:
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
Let us help you find a AI Data Scientist in South America!
Developer prices may vary based on skills and experience requirements as well as availability.
Your project will determine which one is best for you depending on its specifics.
After you hire an AI Data Scientist what kind of tasks can they perform?
Hiring an AI Data Scientist can provide significant value for your business. They can perform a variety of tasks that involve using machine learning, data mining, and programming to interpret and manage data. Here are six example tasks:
- Data Cleaning and Preprocessing
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Model Building and Validation
- Model Optimization
- Predictive Analysis and Reporting
Now, let’s walk through a Python example of connecting to an Oracle database and using the ChatGPT API to predict sales for the next quarter in California from previous customer orders data from the last year. Note that this is a simplified example and in reality, the process would be more complex, involving data cleaning, exploratory data analysis, feature engineering, model training, and evaluation.
# Import required libraries
import cx_Oracle
import pandas as pd
from openai import OpenAI
from openai.api_resources.completion import Completion
# Oracle Database Connection
dsn_tns = cx_Oracle.makedsn('hostname', 'port', service_name='service_name')
conn = cx_Oracle.connect(user='username', password='password', dsn=dsn_tns)
# SQL Query to get previous year's customer orders data in California
query = """
SELECT *
FROM customer_orders
WHERE order_date >= ADD_MONTHS(SYSDATE, -12)
AND state = 'California'
"""
# Execute the query and fetch data into a DataFrame
df = pd.read_sql(query, con=conn)
# Process your DataFrame here, e.g., extracting features, performing EDA, etc.
# After processing and preparing your data, let's say you have a DataFrame 'processed_df'
# Connect to OpenAI GPT-3
openai.api_key = 'your-api-key'
# Let's say 'processed_df' is your processed DataFrame and 'sales' is your target variable
# We can convert this DataFrame to a string format that can be understood by GPT-3
input_data = processed_df.to_dict()
# Construct the prompt for GPT-3 to predict next quarter's sales in California
prompt = f"Predict the sales for the next quarter in California based on the previous year's customer orders data: {input_data}"
# Use the ChatGPT API to generate a prediction
response = Completion.create(engine="text-davinci-003", prompt=prompt, temperature=0.5, max_tokens=100)
# Print the response
print(response.choices[0].text.strip())
This example is a high-level representation of the process and does not include all the steps that would typically be involved in a real-world data science project, such as exploratory data analysis, feature selection, model training, and model evaluation.
Before you hire a AI Data Scientist ask these interview questions!
Can you describe a project where you had to clean and preprocess data before it was ready for analysis?
Data cleaning and preprocessing is a critical step in the data science process. This question aims to assess the candidate’s experience and methods in handling missing data, outliers, and inconsistencies. Their answer can provide insights into their approach to data quality and their understanding of the impact it can have on their models.
Tell me about a time when you had to handle imbalanced data or outliers in your dataset. What approach did you use?
This question is designed to assess the candidate’s experience with and understanding of the challenges associated with imbalanced datasets or outliers. It can reveal their knowledge of various resampling techniques, outlier detection methods, and the impact these issues can have on model performance.
Can you describe a situation where the model you developed did not perform as expected? What steps did you take to improve it?
This question evaluates the candidate’s problem-solving skills, resilience, and their ability to learn from failures. It provides insights into their understanding of model evaluation, their approach to diagnosing issues, and their familiarity with different techniques to improve model performance.
How do you ensure that your models are not overfitting the data?
Overfitting is a common problem in machine learning where a model learns the training data too well, leading to poor performance on new, unseen data. This question helps you understand if the candidate knows how to use techniques like cross-validation, regularization, and early stopping to prevent overfitting.
Can you explain a concept like ‘Gradient Descent’ or ‘Neural Networks’ to a non-technical person?
This question is designed to evaluate the candidate’s communication skills and their ability to explain complex technical concepts in simple, understandable terms. Data scientists often have to communicate their findings and the details of their work to stakeholders who may not have a technical background, so this skill is very important.
Brief History of using AI with Data Science
Artificial Intelligence (AI) and Data Science have had a mutually beneficial relationship over the years. As data has grown in volume, variety, and velocity, the capabilities of AI have expanded to analyze this data and extract valuable insights from it. Conversely, advances in AI have spurred the growth of data science as a field, with new techniques and methodologies being developed to analyze and interpret data.
The Emergence of AI and Early Data Analysis (1950s-1980s)
AI as a field began in the mid-20th century with the advent of the first computers, though the term “Artificial Intelligence” was first coined in 1956. During this period, data was primarily analyzed manually or with simple statistical methods. AI’s early years were marked by optimism about the capabilities of computers to simulate human intelligence, though this enthusiasm was tempered by technical limitations.
The Advent of Machine Learning (1980s-2000s)
The 1980s and 1990s saw the advent of machine learning, a subfield of AI that focuses on the development of algorithms that allow computers to learn from data. This era was marked by the development of foundational algorithms like decision trees, neural networks, and support vector machines. It also saw the emergence of the concept of ‘big data’, highlighting the increasing volume and complexity of data being generated.
The Rise of AI in Data Science (2010s-Present)
The explosion of data in the 21st century has made AI a crucial tool in data science. Advances in computational power and the development of techniques such as deep learning have enabled the analysis of large, complex datasets. Today, AI is used in data science to make predictions, discover patterns, automate data cleaning and analysis, and much more. The integration of AI into data science continues to evolve, promising exciting developments for the future.
Hire a AI Data Scientist from South America with us
As a leading Nearshore Technology Solutions provider, we provide experienced AI data scientists at reasonable prices. High-performance, scalable AI data science solutions are our goal for our clients.
Our AI & data skills and project management experience are utilized to accelerate or rescue data projects. Throughout the project work phase and beyond, we strive to create long-term value.
Since 2014, we’ve matched skillful AI data scientists with great teams for over a hundred startups and tech companies worldwide.
As devoted members of your team, our AI Data Scientists integrate seamlessly with your organisation.
In order to ensure the quality of AI Data Scientists we supply, we go through a vetting process to verify their communication skills, remote working readiness, and technical skills. We not only check for depth in AI and Data Science, but we also look for breadth in problem solving and experience.
Lowered Costs
Outsourcing is usually done to reduce costs for companies. It is cheaper to hire AI Data Scientists from outside the United States. The overall cost of employment will also be significantly reduced. You won’t have to pay employment taxes, benefits, redundancy liabilities, or office space in the US or Europe.
Scale Up Quicker
A fast expansion or downsizing can create a great competitive advantage for any business. Your business can scale up or down with us as you need, and with relative ease.
AI Experience
Your project is in the hands of a company with extensive experience helping businesses achieve their goals when you outsource AI Data science. Your data project will be delivered on schedule and within budget, so you can feel more secure.
How do we test a AI Data Scientist before we hire them?
Testing an AI Data Scientist before we hire them involves a combination of technical evaluations, problem-solving tasks, and behavioral assessments. We give them a real-world dataset and ask them to perform tasks such as data cleaning, exploration, modeling, and interpretation of the results. This allows us to assess their technical skills, familiarity with tools and technologies, and understanding of data science principles.
We may give them a problem-solving task might involve a theoretical scenario or a data science problem where they must outline their approach to solving it, revealing their analytical thinking and problem-solving skills. Finally, during the interview, we pay attention to their communication skills, their ability to explain complex concepts in simple terms, and how they handle feedback or criticism. All of these components together gives us a comprehensive view of their capabilities as a data scientist.
Once hired, how do you efficently manage AI Data Scientists?
Managing AI Data Scientists efficiently requires a combination of technical understanding, effective communication, and strategic planning. Here’s how you can do it:
Understanding Technical Requirements
As a manager, you should strive to understand the technical aspects of the projects your team is working on. This knowledge can help you make informed decisions, support your team effectively, and communicate with stakeholders.
Setting Clear Goals
Setting clear, measurable goals can help your team focus their efforts and understand what they are working towards. These goals should be aligned with the broader objectives of the organization.
Encouraging Continuous Learning
The field of AI and Data Science is constantly evolving. Encouraging your team to continuously learn and stay updated on the latest trends and technologies can boost productivity and innovation.
Facilitating Communication
Promoting open communication within the team and with other stakeholders can ensure that everyone is on the same page. Regular meetings and updates can help track progress and address any issues promptly.
Providing the Necessary Tools and Resources
Ensuring your team has access to the necessary hardware, software, and data resources can improve their efficiency and productivity. Additionally, providing training or funding for professional development can be a significant morale booster.
How long does the median AI Data Scientist job last?
On average, AI Data Scientists in the US work between 1.4 and 2.9 years, with larger companies tending to keep workers longer. In Brazil, the average period of time spent ranges from 2.1 to 3.8 years, with those in Sao Paulo spending the least time in the job. Someone used to spend their entire career at one company in the past.
In spite of the fact that longevity is no longer regarded as a strong characteristic among seniors AI Data Scientists, they may transition from job to job fairly quickly, seeking new opportunities and more money. This market has a low unemployment rate and a high demand for professionals with AI, Data Science, and other vital skills. The most in-demand AI data pros can migrate between jobs (or even freelance) fairly easily. The importance of communication between employees and perks to attract and retain top performers cannot be overstated.
Frequently Asked Questions (FAQs)
Our services are trusted by hundreds of startups and tech companies worldwide, and we have matched over a hundred skilled AI data scientists to great development 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 AI and breadth across the greater programming domain.
Our AI Data Scientists hold advanced degrees in fields such as Computer Science, Statistics, or related disciplines. They have extensive experience working on diverse AI projects and are proficient in tools and languages such as Python, R, SQL, TensorFlow, and PyTorch. They also have a strong foundation in machine learning, deep learning, and statistical modeling.
We take data security and privacy very seriously. Our Data Scientists are trained in data handling best practices, and we have strict policies in place to ensure the security of your data. We comply with all international data protection regulations and can work with you to meet any specific security requirements you may have.
We use a variety of tools to facilitate communication, such as email, video conferencing, and project management software. We ensure regular updates and meetings to keep you informed about the project’s progress. Our team is also available to adjust their working hours to overlap with yours for better collaboration.
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.
Absolutely! We offer post-project support and maintenance services. This can include helping with the integration of the AI model into your existing systems, making adjustments based on new data or changes in the business environment, and troubleshooting any issues that may arise.
