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Purushothama

Purushothama

Data Scientist

Experience18 years
AvailabilityFull-time

Purushothama has 18 years of experience in Java 8/11, Spring boot, Microservices, NoSQL (Cassandra, MongoDB, DynamoDB , GraphDB), Kafka, AWS, and Cloud Related Frameworks.

Expert in
  • Data Science
  • Core Java
  • Java
  • SQL
  • Spring
Also worked with
  • Mockito
  • MySQL
  • Jenkins
  • Java 8
  • SOAP
Muhammed

Muhammed

Data Scientist

Experience20 years
AvailabilityFull-time

Muhammed has 20 years of experience in data science and software development. He is passionate about AI and transforming its research into impactful real-world applications

Expert in
  • Python
  • SQL
  • Data Engineering
  • Data Science
  • Machine Learning
Also worked with
  • PostgreSQL
  • Spark
  • Java
  • HDFS
Dan

Dan

Data Scientist

Experience8 years
AvailabilityFull-time

Dan has 8 years of experience in software development and implementation of data management systems / platforms. He was recognized among the Top 30 entrepreneurs in the UK, by Startups Magazine, and among the Top 27 Most Disruptive Entrepreneurs in the UK, by The Telegraph

Expert in
  • Team Management
  • SQL
  • Data Science
  • Data Engineering
Also worked with
  • Python
  • Machine Learning
Jamie

Jamie

Data Scientist

Experience5 years
AvailabilityFull-time

Jamie has 5 years of experience as data scientist and software engineer. He has extensive knowledge of technologies such as JavaScript ES6, Hadoop, Tableau, SQL, Git, etc.

Expert in
  • JavaScript ES6
  • Hadoop
  • Tableau
  • SQL
  • Git
Also worked with
  • DevOps
  • Node.js
  • Java
Morteza

Morteza

Data Scientist

Experience9 years
AvailabilityFull-time

Morteza has 9 years of experience in different fields of software development, from embedded systems to frontend engineering. He is highly skilled in technologies such as Python, PostgreSQL, Redis, SQL, Ruby, Ruby on Rails, etc.

Expert in
  • Data Science
  • PostgreSQL
  • Redis
  • SQL
  • Ruby
Also worked with
  • CSS
  • HTML
  • Rust
  • Machine Learning
  • Python
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How to hire the best data scientists?

Learn about the skills to look for, interview questions, and more while hiring data scientists from the huge pool of talented developers.

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How to hire the best data scientists?
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How to hire a data scientist? Skills to look for, interview questions, and more

It can be challenging to hire data scientists for your company at times. Although it is currently the most in-demand skill in the industry, finding a skilled data scientist for hire is not as simple as it may appear. That's why we're here to assist businesses looking to hire data scientists on their own.

The process to hire a data science developer can be quite complex, especially if you are not from the relevant technical background. However, Turing provides highly-skilled data scientists for hire, helping you onboard developers within 4 days.

Additionally, if you're from a non-software development background and want to learn more about hiring a data scientist, we've put up an excellent resource for you.

Skills to look for in a data scientist?

It can be challenging to hire data scientists for your company at times. Although it is currently the most in-demand skill in the industry, finding a skilled data scientist for hire is not as simple as it may appear. That's why we're here to assist businesses looking to hire data scientists on their own.

The process to hire a data science developer can be quite complex, especially if you are not from the relevant technical background. However, Turing provides highly-skilled data scientists for hire, helping you onboard developers in 4 days.

Additionally, if you're from a non-software development background and want to learn more about hiring a data scientist, we've put up an excellent resource for you.

1. High-level proficiency in Python

Python is the most common coding language required for data scientist roles. Because of its versatility, developers use this language for most of the steps involved in data science processes. It can take various formats of data, and developers find it easy to import SQL tables into codes with Python. It also allows developers to create datasets, and they can easily source any type of dataset they need on Google. So, when you hire a data scientist, don’t forget to check if they’re well-versed in Python or not.

2. Familiarity with the Hadoop platform

Although this isn't a definitive requirement, many companies look for candidates with familiarity with Hadoop. Data scientists may encounter a situation where the volume of data they have exceeds their system's memory, or they need to send data to different servers; this is where knowledge of Hadoop comes in handy. They use Hadoop to convey data to various points on a system quickly. Moreover, Hadoop is useful for data exploration, data filtration, data sampling, and summarization. Therefore, if your project is more data-intensive, and you are going to heavily rely on your data scientist for data management, you should pick a candidate who has an in-depth understanding of Hadoop.

3. Good understanding of SQL Database

Even though NoSQL and Hadoop cover large components of data science, you can still expect a candidate to write and execute complex queries in SQL. SQL is a programming language that helps data scientists carry out operations like adding, deleting, and extracting data from any database. It also carries analytical functions and transforms database structures.

SQL was created to assist businesses in accessing, communicating, and working with a short and large amount of data. When you use it to query a database, it provides you with information. SQL also has short commands that can save you time and reduce the amount of code required to run complex searches, making it an indispensable skill for data scientists. Hence, when you are looking for data scientists for hire, do check their SQL knowledge.

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4. Good understanding of Apache Spark

Spark is quickly becoming the most popular big data technology around the globe. Similar to Hadoop, it is a big data processing framework. The sole difference between Spark and Hadoop is that Spark is quicker. This is because Hadoop reads and writes to disc, slowing it down, whereas Spark caches its computations in memory. Apache Spark was created primarily for data science to speed up the execution of complex algorithms. When dealing with a large amount of data, it helps in dispersing data processing and saves time. It also helps data scientists in dealing with large amounts of unstructured data and avoiding data loss. Therefore, if you hire data scientists, gauge their Apache Spark skills well.

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5. Hands-on experience with data visualization

The corporate world generates a large volume of data regularly. This information must be converted in a simple to interpret manner. Raw data is more difficult for people to comprehend than images in the form of charts and graphs. Your potential data scientist must be able to visualize data with the aid of data visualization tools such as ggplot, d3.js and Matplottlib, and Tableau. These tools help data scientists convert complex results from projects to a format that will be easy to comprehend. If you hire data scientists, ensure that they are adept at data visualization to aid your organization in deriving key insights seamlessly.

6. Hands-on experience with data wrangling

Often the data a business acquires or receives is not ready for modeling. It is, therefore, important to understand and know how to deal with the imperfections in data. Data wrangling is the process where data scientists prepare data for further analysis, transforming and mapping raw data from one form to another to derive insights. For data wrangling, data scientists acquire data, combine relevant fields, and then cleanse the data. Moreover, this process also enables data scientists to focus more on data analysis rather than the cleaning part and ultimately lead data-driven decision-making in a direction supported by accurate data. Therefore, data wrangling is a significant skill to assess when you hire a data scientist.

7. Basic understanding of machine learning and AI

A vast number of data scientists lack expertise in machine learning techniques and topics. Neural networks, reinforcement learning, adversarial learning, and other techniques are examples of this. Suppose you want to make your team stand out from others. In that case, you'll need to hire data scientists who are familiar with AI and machine learning techniques like supervised machine learning, decision trees, logistic regression, unsupervised machine learning, time series, natural language processing, outlier detection, computer vision, recommendation engines, and survival analysis, among others.

Create a hiring funnel

Creating a hiring funnel will provide you with numerous benefits, like assisting you in identifying the top skills and identifying a Data Scientist who will fit into your company's culture.

What Turing does for you

Candidates screening
Candidates screening

We will help you select the best talents and spot a Data Scientist who will fit in your company culturally

Test task
Test task

We verify if the candidate really wants to work at your company and is able to spend 5+ hours to prove it by rigorous tests. It helps us to see a developer's caliber.

Technical test
Technical test

Developers are asked data science related questions and made to solve tricky problems. We use open questions. The goal is not only to test developers’ knowledge – we also want to find out their way of thinking.

Giving specific feedback
Giving specific feedback

We provide explicit feedback on both the test task and the technical test after we have checked the developer's expertise.

What you do

Interview
Interview

You can interview the shortlisted developers to check if the candidate matches your requirements and is a good fit for your company.

Hired/Not-hired
Hired/Not-hired

Hire intelligently with developers sourced by software, vetted by software, matched by software & managed by software.

Top interview questions to hire data scientists

Hiring proficient data scientists is key to harnessing the power of data for business success. Here are some sample interview questions to hire data science developers, which you can use to properly evaluate their skills and recruit the ideal candidate.

A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. In data science models, it's very useful. So, when you hire data scientists, you must check if they understand what a decision tree is and what are the steps to make one.

Here’s how the time series problems differentiate from other regression problems:

  • Time series data can be thought of as an extension to linear regression which uses terms like autocorrelation, and movement of averages for summarizing historical data of y-axis variables for predicting a better future.
  • Forecasting and prediction is the main goal of time series problems where accurate predictions can be made but sometimes the underlying reasons might not be known.
  • Having Time in the problem does not necessarily mean it becomes a time series problem. There should be a relationship between the target and time for a problem to become a time series problem.
  • The observations close to one another in time are expected to be similar to the ones far away which provides accountability for seasonality. For instance, today’s weather would be similar to tomorrow’s weather but not similar to the weather from 4 months from today. Hence, weather prediction based on past data becomes a time series problem.

Overfitting is used to describe a model that is only trained on a tiny quantity of data and ignores the wider picture. To avoid overfitting, there are three major ways. So your interviewee can answer just the method names or explain each one by one. You can ask to add examples with each; there, you will know how much the candidate knows about this data science model.

Dimensionality reduction is the process of converting a vast data set into smaller data (fields) in order to convey similar information more simply. When the candidate is done with this kind of simple explanation, ask him/her to mention the benefits of dimensionality. In this way, you can measure the depth of the candidate's knowledge in the data science subject.

K-means is a method of vector quantization, which is quite essential for the clustering of data. For any data scientist, it is essential to know data science techniques, and one of the important ones is K-means. He or she may answer in the following manner: To choose k for k-means clustering, we utilize the elbow approach. The elbow approach works by doing k-means clustering on the data set, where 'K' is the number of clusters. It is defined as the sum of the squared distances between each member of the cluster and its centroid in the sum of squares (WSS).

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Frequently Asked Questions

The purpose of the two-week no-risk trial period is to start working with the developers and include them in the team. If you are satisfied with the developers, you keep working with them and pay their salary including the first two weeks. But, if you are not satisfied during the trial period, then you won’t pay anything.

Turing offers top-quality, cost-effective, and highly productive data scientists who belong to the top 1% of the world's remote developers. All Turing data scientists are selected only after going through a series of rigorous tests where their skills are deeply vetted. Daily standups are mandatory for every Turing developer as they keep the developer and the customer in alignment with the discussed goal. All Turing remote data scientists work for at least 4 hours in your time zone for your convenience.

If you are looking for the best data scientists who are willing to work in your timezone, try Turing. Our AI-powered deep-vetting talent platform sources pre-vetted data scientists from across the world and provides them at half the cost.

Turing has created the first and only AI-powered deep-vetting talent platform to vet remote developers. Turing tests developers based on actual skills vs. self-reported experience from traditional resumes or job interviews. Every developer at Turing has to clear our tests for programming languages, data structures, algorithms, system designs, software specialization, frameworks, and more. Each Turing developer goes through our automated seniority assessment test comprising 57 calibrated questions in 5 areas — project impact, engineering excellence, communication, people, and direction.

With Turing, you can hire the best remote developers for 100+ skills such as React, Node, Python, Angular, Swift, React Native, Android, Java, Rails, Golang, PHP, Vue, DevOps, Machine Learning, etc. Turing also offers developers based on tech stack and seniority.

There is no doubt about the fact that data scientists are in demand. The largest companies in the world are data science-fueled enterprises. Be it Google, Amazon, and Facebook, each use data science to create algorithms that improve customer satisfaction and maximize their profits. With Turing, companies can now build a team of the best remote data scientists in a matter of 4 days.

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