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Hire Data Scientists remotely with Strider

Effortlessly hire remote Data scientists through Strider. Access our network of vetted professionals and let our AI curation engine match you Data scientists who match your specific needs.

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How it works

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We will learn more about your unique requirements, so we can share a shortlist of pre-vetted with you.

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Review detailed profiles, and meet them over a video call. Then, choose who you'd like to join your team.

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Hire with the click of a button, and start building the future together with your new . We take of everything else like paperwork, equipment, and more.

Why Strider is the best way to hire Data Scientists

Strider's vetting process
Top Talent

Data scientists on Strider are pre-vetted for soft skills, English communication skills, and tech skills. Hire only the best.

Candidates that match your needs
Efficient

Strider clients typically hire in 1-2 weeks because we quickly and accurately match you with the right pre-vetted Data scientists.

Candidates network
Cost Effective

Work with Data Scientists based in Latin America who speak fluent English to save 30-50% on software development costs.

Data Scientists for hire, and more!

Whether you're looking for Data Scientists today, or tomorrow, we have you covered. s in our network have experience across hundreds of technologies.

Luiza F. Back-end Developer

Proficient in various programming languages and frameworks being able to excel in leading cross-functional teams, architecting scalable solutions, and delivering high-quality products.

C#
Kotlin
Microsoft SQL Server
Diego V. Full-stack Developer

Experienced developer with varied background in big companies and startups. Proficient in designing and executing complex web apps with extensive grasp of front-end and back-end technologies.

C#
Kotlin
Microsoft SQL Server
Caainã J. Full-stack Developer

Successfully delivered a wide range of web applications, showcasing proficiency in front-end and back-end technologies, with more than 10 years of coding from concept to deployment.

C#
Kotlin
Microsoft SQL Server
Bianca S. Full-stack Developer

With over five years of experience in web development, a focus is placed on supporting companies in the building and sustaining of a robust code base using cutting-edge technologies.

C#
Kotlin
Microsoft SQL Server
React
Vue
Ruby on Rails
Angular
Python
Node.Js
C#
PHP
Typescript
Swift
Android
Kotlin
Go
C++
Laravel
and 100+ other technologies

Frequently asked questions on how to hire with Strider

No, it's 100% free to get started with Strider. You only pay if you hire, and there is no obligation to hire.

We've found that most customers end up saving 30-50% compared to hiring an equally talented based in the US. When you speak with our hiring experts, they'll get to know more about your role in order to provide an accurate quote.

After your initial call with our hiring experts, we will share a curated shortlist of within two business days. Companies we work with typically make a hire within 1-2 weeks after receiving the shortlist. Though, this process can move as fast as you want. Some companies make a hire within a few days after receiving the shortlist.

Yes, we also work with other technology roles like designers, QA, DevOps, and more.

We work with virtually every modern technology stack. You'd be hard-pressed to find a technology we do not cover.

Yes, as a part of our vetting process, we verify that the has advanced English skills, so that they can keep up in fast-paced, English-speaking workplaces.

All of our work remotely from Latin America. They speak fluent English and work in US time zones. We handle local compliance, so you don't have to worry about the legal aspects and can stay focused on your business.

We vet for soft skills, technical skills, and English fluency. This ensures that they'll be able to excel in a remote, US-headquartered work environment.

Hire Data Scientists Effectively in 2023

Data scientists are highly skilled professionals who specialize in extracting meaningful insights and patterns from vast amounts of data. They possess expertise in statistics, machine learning, and data analysis techniques. Data scientists play a vital role in today's data-driven world, helping organizations make informed decisions, optimize processes, and drive innovation across various industries. Their ability to transform complex data into actionable insights has made them indispensable in fields such as finance, healthcare, e-commerce, and marketing.

In addition to traditional hiring options, there are now remote and freelance opportunities for hiring data scientists. Remote data scientists offer flexibility and access to a global talent pool. Organizations can leverage the expertise of data scientists from around the world, working remotely on projects and delivering valuable insights. These remote and freelance options provide cost-effective ways to collaborate with top data scientists and harness the power of data for business success.

What to look for when hiring data scientists?

Technical Skills

When hiring a data scientist, several key technical skills should be considered to ensure they are equipped to handle complex data analysis tasks. Here are the most important technical skills to look for:

Statistical Analysis

A strong foundation in statistics is essential for a data scientist. They should be proficient in statistical techniques such as hypothesis testing, regression analysis, and experimental design, enabling them to derive meaningful insights from data.

Machine Learning

Data scientists should possess expertise in machine learning algorithms and techniques. This includes knowledge of both supervised and unsupervised learning, as well as experience with popular machine learning frameworks and libraries like scikit-learn and TensorFlow.

Programming languages

Proficiency in programming languages such as Python or R is crucial for data scientists. They should be adept at data manipulation, cleaning, and analysis using programming tools, as well as have the ability to develop scalable and efficient code for data processing tasks.

Data Visualization

Data scientists need to effectively communicate their findings and insights to stakeholders. Strong data visualization skills, including the use of tools like Tableau or Matplotlib, enable them to create clear and visually appealing representations of complex data sets.

Big Data Technologies

With the exponential growth of data, knowledge of big data technologies is valuable. Familiarity with tools like Apache Hadoop, Spark, or SQL-based databases allows data scientists to handle and process large-scale data sets efficiently.

Domain Knowledge

Understanding the specific domain or industry in which the data scientist will be working is beneficial. Domain knowledge helps data scientists better interpret and contextualize the data, resulting in more accurate analyses and actionable insights.

While these are not the only important technical skills for a data scientist, they provide a solid foundation for handling various data-related tasks and driving impactful outcomes.

Soft Skills

In addition to technical skills, data scientists also require a set of essential soft skills to excel in their roles:

Critical Thinking

Data scientists need strong critical thinking skills to analyze complex problems, identify patterns, and make informed decisions. They should be able to approach data analysis with a logical and analytical mindset, considering various perspectives and potential solutions.

Collaboration

Data scientists often work in multidisciplinary teams, collaborating with data engineers, domain experts, and business stakeholders. Effective collaboration skills are crucial for successful teamwork, as data scientists need to communicate their findings, listen to others' perspectives, and integrate their insights into broader projects.

Communication

Clear and effective communication is essential for data scientists to convey complex concepts and findings to both technical and non-technical audiences. They should be able to present their analyses and insights in a concise and understandable manner, using data visualization and storytelling techniques to convey key messages effectively.

Adaptability

The field of data science is dynamic and constantly evolving. Data scientists need to be adaptable and open to learning new technologies, tools, and techniques as they emerge. They should be comfortable with ambiguity, able to embrace new challenges, and willing to continuously update their skills to stay ahead in the field.

These soft skills complement the technical expertise of data scientists and enable them to tackle complex data problems, work effectively in teams, and communicate their findings to drive informed decision-making within organizations.

Specialized Skills

In addition to the core technical and soft skills, there are unique and specialized skills that can set data scientist candidates apart in the field. These skills demonstrate a deeper level of expertise and contribute to specific applications of data science:

Natural Language Processing (NLP)

Proficiency in NLP techniques is valuable for data scientists working with unstructured textual data. NLP skills enable them to extract meaning, sentiment, and insights from large volumes of text, which is crucial for applications such as sentiment analysis, text classification, chatbots, and language translation.

Deep Learning

Expertise in deep learning algorithms and architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), allows data scientists to handle complex patterns and relationships in data, particularly in image recognition, speech recognition, and natural language understanding tasks. Deep learning skills are essential for advanced applications like computer vision, speech processing, and language generation.

Time Series Analysis

Specialized skills in analyzing and modeling time-dependent data are valuable for data scientists working in fields like finance, forecasting, and sensor data analysis. Time series analysis enables them to capture temporal patterns, identify trends, and make accurate predictions, contributing to applications like stock market forecasting, demand forecasting, and anomaly detection.

These specialized skills enhance the capabilities of data scientists and enable them to tackle specific data challenges in various domains. By leveraging NLP techniques, deep learning algorithms, and time series analysis, data scientists can provide more advanced and tailored solutions in their respective applications, ultimately driving valuable insights and decision-making for organizations.

Top 5 Data Scientist Interview Questions

Here are five interview questions for data scientists along with their purposes and what a good candidate should cover in their answers:

What is the difference between supervised and unsupervised learning in machine learning?

This question assesses the candidate's understanding of the fundamental types of machine learning and their ability to explain complex concepts.

A good candidate should explain that supervised learning involves training a model using labeled data to make predictions, while unsupervised learning involves discovering patterns and structures in unlabeled data without specific target outputs. They should provide examples of algorithms and use cases for each type.

How would you handle missing data in a dataset?

This question evaluates the data scientist's data preprocessing skills and problem-solving abilities when dealing with missing values.

A good answer should demonstrate knowledge of various strategies for handling missing data, such as imputation techniques (mean, median, mode), deletion methods (listwise, pairwise), or advanced techniques like regression imputation or using machine learning algorithms. They should discuss the pros and cons of each approach and their suitability for different scenarios.

Explain the concept of dimensionality reduction and its importance in machine learning.

This question examines the candidate's understanding of dimensionality reduction techniques and their ability to explain the significance of reducing the number of features.

A good candidate should define dimensionality reduction as the process of reducing the number of features in a dataset while preserving relevant information. They should explain the benefits, such as reducing computational complexity, avoiding the curse of dimensionality, and improving model performance. They should also mention popular techniques like principal component analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE) and explain their applications.

What is the purpose of regularization in machine learning models?

This question assesses the candidate's knowledge of regularization techniques and their understanding of preventing overfitting.

A good candidate should explain that regularization is used to prevent overfitting, a common issue where a model performs well on training data but fails to generalize to new data. They should discuss the purpose of regularization terms like L1 (Lasso) and L2 (Ridge) regularization, which help control the complexity of the model by penalizing large parameter values. They should also mention the trade-off between bias and variance and the impact of regularization on model performance.

How would you evaluate the performance of a machine learning model?

This question tests the candidate's knowledge of model evaluation metrics and their ability to select appropriate metrics for different scenarios.

A good candidate should mention common evaluation metrics such as accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). They should explain that the choice of metrics depends on the problem type (classification, regression) and the specific goals of the project. They should also discuss the importance of cross-validation, model selection techniques, and the need to consider business context when interpreting performance metrics.

Common questions about hiring Data Scientists

Deciding whether to hire data scientists on full-time or a freelance basis depends on several factors. Hiring a full-time data scientist can be beneficial when you require dedicated expertise and long-term involvement in data science projects. Full-time data scientists offer continuity, deeper integration into the company's culture, and the ability to work closely with other team members.

On the other hand, hiring a freelance data scientist can be advantageous for specific projects, short-term needs, or when there is a fluctuating demand for data science expertise. Freelancers offer flexibility, specialized skills, and the ability to bring fresh perspectives from working with different clients and industries. They can be cost-effective, as you only pay for their services when needed.

Ultimately, the decision between hiring a full-time data scientist or a freelance data scientist depends on the specific requirements, duration, and nature of the data science work needed.

While both data scientists and data analysts work with data, there are distinct differences in their roles and responsibilities. Data scientist and data analyst have different skill sets and focus on different aspects of the data analysis process. Data analysts primarily focus on examining and interpreting data to uncover insights, trends, and patterns. They often work with structured data and use various statistical and analytical techniques to extract meaningful information. Data analysts are skilled in data visualization, data cleaning, and basic statistical analysis.

Data scientists have a broader scope and possess advanced skills in statistics, programming, and machine learning. They not only analyze data but also develop complex algorithms and predictive models to derive insights and make data-driven predictions.

To hire a data scientist, it's important to be aware of the potential challenges to make informed decisions. Here are some common challenges associated with hiring data scientists:

  • Data science is a rapidly growing field, and there is a high demand for skilled professionals. However, there is a shortage of qualified data scientists in the market, making it challenging to find and attract top talent.
  • Due to the high demand for data scientists, there is fierce competition among organizations to hire the best candidates. Large tech companies and well-funded startups often have the advantage of offering attractive compensation packages and perks, making it difficult for smaller companies to compete.
  • Evaluating the technical skills and abilities of data scientists can be complex. The hiring process often involves assessing knowledge in areas such as programming, statistics, machine learning, and domain expertise. Designing effective assessments and conducting interviews that accurately measure these skills can be a challenge.
  • Data scientists not only need strong technical skills but also need to fit well within the organization's culture. They should be able to collaborate with other team members, communicate effectively, and align with the company's goals and values. Assessing cultural fit during the hiring process can be subjective and challenging.

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