Want to hire the best data scientists? Here’s a list of the top 30 data scientists, the skills you must look for, and how to hire them!
Data Scientist is one of the most in-demand jobs in 2020. Let us start with what is a data scientist? A data scientist uses specialized skills to solve complex problems. Data scientists are data wranglers, who gather and analyze large sets of structured and unstructured data.
A data scientist is an analytical expert, who use their expertise in technology and social science to discover trends and manage data. They are capable of uncovering solutions to various business challenges.
But Why hire a data scientist? Companies today hire data scientists to make better business decisions and improve their growth. Companies use the data to make concrete decisions and identify new opportunities.
In this blog, we will attempt to cover the steps involved in hiring a data scientist. We will provide some data scientist interview questions and answers. We will discuss the data scientist salary standards and other handy tips to help you in the hiring process.
Market for a data scientist
According to the Bureau of Labor Statistics, there should be a growth in data science jobs. Analysts predict the data scientist job outlook is a growth of 16% from 2018 to 2028, which is faster than the average of other jobs. This growth will translate to approximately 5600 jobs.
Data scientists are in demand in every organization. Government organizations, software, multinational companies require a data scientist. Companies rely on big data to serve their customers better, build products, and improve operations. Government organizations monitor the effectiveness of policy decisions by analyzing big data. However, there is a shortfall of qualified candidates.
Howard Business Review ranks data scientists as the number 1 job on 25 best jobs in America. Technology is evolving rapidly, the demand for data scientists will also increase. The rapid collection of data will increase the demand for data mining services.
Things to note while hiring a data scientist
Now that you know what does a data scientist do, here are some things to note while hiring a data scientist,
- Have a clear understanding of why you want to hire a data scientist – Start by identifying the business problems you want them to address. The data you have should be related to the business problem you expect them to solve.
- Check their background in business intelligence, analytics, or data-driven decision making – Data scientists generally have a background in STEM, science, applied mathematic, and statisticians.
- Know programming language – A data scientist needs to know how to use programming languages such as python, R, and Matlab used by scientists.
- Know to use statistic tools – A data scientist should be able to use data management tools such as SQL. The data scientist should be adept at using the database systems.
- Understand data science profile and know what you want to hire – Data scientists can have experience in different professions (marketer, designers, product managers, etc). Some data scientists have machine learning models or big data models.
- Define what you want them to do – A data scientist should have a proper job description that focuses on the company instead of the problems.
- Build a data-driven culture that favor data scientist – For a data scientist to be effective your company needs to have the infrastructure and budget support. Your company needs to experience in working with data.
- Decide where and how they will fit in your organization – the data scientist needs support from department heads in departments like the product, technology, and business departments. The data science team should be able to provide feedback and help build accountability in the team.
- Have a supporting or dedicated product manager – A data scientist cannot work in isolation and needs a team to deliver results. Product managers regularly assist data scientists by providing data required like customer insights, data analysis, etc.
- Clear communicator – A data scientist should be able to translate the data into useful information. The data should become actionable and be able to provide a road map.
Qualifications of best data scientist
Data Scientist is a specialized field. 88% of data scientists are have completed their masters and around 44% of data scientists are PhDs. Data scientist typically has a bachelor degree in computer science, social science, physical science, and mathematics and statistics.
32% of data scientist has completed their mathematics and statistics. The best universities to complete undergraduate mathematics and statistics degree are,
- Stanford University
- Princeton University
- Massachusetts Institute of Technology
- University of California and Berkeley
- New York University
The best universities to complete masters of data scientists are,
- Columbia University
- Stanford University
- University of Pennsylvania
- Duke University
- University of Southern California
Skills and qualities to look for in data scientists
A data scientist should have technical and soft skills. When you hire data scientists, we have listed the skills and qualities you can look for when you look at experiences level data scientist or entry-level data scientist resumes,
- Probability and Statistics – Part of a data scientist job description is to use capital processes, algorithms, and system to extract knowledge, insights, and to make informed decisions. Probability and statistics help scientists use probability and statistical methods to analyze data and make better estimates.
- Programming knowledge – Though data scientist needs to understand the basics of machine learning. But a data scientist needs to know programming languages to communicate with the computer. Even an entry-level data scientist resume should show knowledge of languages like Java, SQL, Scala, Matlab, Python, R, or Julia.
- Data Manipulation and Analysis – Data manipulation and wrangling allow data scientists to clean the data. The data manipulation will help your firm make data-driven decisions. A data scientist analysis the data to answer questions you may have regarding the data set.
- Data Visualization – Data visualization helps data scientists build a story from the data. It helps to get an overall picture of the data.
- Machine Learning – Data scientists use machine learning to build predictive models. Some of the common data models used by data scientists are:-Random Forest, XG Boost, CatBoost, etc.
- Big Data – Today organizations have to handle large quantities of data or big data. Data scientists help you use big data platforms such as MongoDB, Oracle, Microsoft Azure, and Cloudera.
- Cloud-computing – Data scientists today use cloud computing products and services to process and get access to the resources needed to manage and process data.
- Communication skill – Data scientists need to be able to communicate effectively. They should be able to formulate the correct problem statement and effectively present data.
- Structured thinking – Structured thinking is a critical skill for a successful data scientist. The scientist should be able to break down a problem to find an effective solution.
- Curiosity – A data scientist should be curious and continuously ask questions.
Certifications to look for in data scientists
Data scientists are specialists. Certifications can give junior data scientist an edge and can compensate for their lack of experience. Here are the top certifications for when you look for a top data scientist,
- Certified Analytical Professional (CAP) – The certified analytical professional is a vendor-neutral certification that helps to certify the data scientist’s ability to transform complex data into valuable insight and action.
- Google Professional Data Engineer Certificate – This certification is ideal if you have a Google Cloud Platform in your organization. The Google Professional Data Engineer Certificate shows the data scientist can design and manage solutions in the Google Cloud Platform.
- Microsoft Certified Azure AI Fundamentals – Microsoft Azure AI Fundamental certification validates the scientist’s knowledge of machine learning and artificial intelligence using Microsoft Azure services. Microsoft also offers Microsoft Certified Azure Data Scientist Associate.
- Cloudera Certified Associate Data Analyst – The Cloudera Certified Associate Data Analyst certification will demonstrate the ability of the developer to use SQL and generate reports in the Cloudera CDH environment.
- Data Science Council of America Senior Data Science – The program is meant for scientists with five or more experience in research and analytics. Data Scientist with ten or more years of experience often opts for Data Science Council of America (DASCA) Principal Data Scientist (PDS).
- Dell EMC Data Science Track (EMCDS) – Dell EMC Data Science Track offers two certifications – Data Science Associate v2 (DCS – DS) and Data Science specialist certification. The comprehensive course covers Hadoop, Pig, HBase, and visualization methods.
- IBM Data Science Professional Certificate – The IBM Data Science Professional Certificate is a comprehensive certificate that shows the scientist’s competence in using SQL, Python, Databases, data analysis, data visualization, and machine learning.
- Open Certified Data Science (Open CDS) – The Open Certified Data Science (Open CDS) – This certification is offered to experienced data scientists.
- SAS Certified AI & Machine Learning Professional – SAS Certified AI & Machine Learning Professional Certification demonstrates the data scientist’s ability to use open-source software using AI and analytics skills. SAS offers additional certifications:- SAS Certified Big Data Professional and SAS Certified Data Scientist.
Data scientist rate per hour
A data scientist can bring a lot of value in addition to your company. The salary of a data scientist can depend on several factors such as experience, job title, specialization, education, location, and organization. The average salaries of a data scientist in the US are as follows.
- Entry level data scientist salary: $ 95,000/year.
- Mid level data scientist salary: $130,000/year
- Senior level data scientist salary: $ 165,000/year
The data scientist salary per hour ranges from $31 to $36. The average consultancy rates in the US for data scientists can vary widely. Some of the typical rates are as follows.
- Machine learning consulting rates can vary a Ph.D. consultant can charge anyway from $200/hour to $1000/hour. While an entry-level consultant charges an average of $100/hour.
- Hadoop consulting rates, is usually $ 300/hour.
- Big data consulting rates usually ranges from $ 46 to $57/hour.
The average salary of a data scientist in the UK are,
- Junior data scientist salary is £25,000 to £30,000
- Mid-level data scientist salary £40,000 and £60,000.
- Senior level data scientist salary £100,000
- Hadoop billing rate for a consultant £450/day
- Bid data consultancy rates $100-149/hour
- Machine learning consultancy rates £450/day
The data scientist salary per hour is £29.67. Data analyst vs data scientist salary in the UK is £47,500 vs £50,000 respectively. The average salary of a data scientist in India are,
- Junior data scientist salary INR 364,500/year
- Mid level data scientist salary INR 793,400/year
- Senior level data scientist salary INR 20,00,000/year
- The data scientist salary per hour is INR 1017
Places to find the best data scientist
Before you start hiring data scientists process, you need to decide if you will get your data statistical analysis firm is hired to analyze, data science contractor, or freelancer. Some different types of data scientists are BLS data scientist and a full-stack data scientist. You may opt to perform a search for data scientist jobs near me to look for local talent. Some of the best job portals to hire data scientist are,
- Indeed – It is one of the oldest job portals. Whether you are looking for candidates for a senior-level data scientist or entry-level data scientist jobs.
- unremot.com – Enables you to find a data scientist for hire. You can view profiles, contact shortlisted candidates through video calls, messages, and chats.
- LinkedIn – LinkedIn is a social networking site for professionals. You can get access to the best data scientists.
- Hired – Hired is a job portal that has vetted candidates, with easy to use filters.
- GitHub Jobs –It allows you to access the data scientist community and post job posting.
- Dice – It is an easy to use portal allowing to hire data scientists.
- SimplyHired – It is a job portal for both full-time candidates or freelance data sciences. You can find data scientist entry-level candidates or experienced candidates.
- Upwork – Upwork is one of the largest job boards for freelance data scientists.
- Toptal – This network for freelance data scientists you can find Microsoft data scientist or Google data scientist or Facebook data scientist on the website.
- Freelancer – Freelancer is a website that allows you to post requirements for freelance jobs.
Steps involved in hiring the best data scientist
The hiring process for a specialized data scientist can be unnerving. You need to decide if you need the benefits of hiring a data analyst vs data scientist. However, breaking down the hiring process into some concrete steps can help to hire a data scientist or a data scientist intern.
- Write a detailed job description.
- Prepare a list of interview questions for data scientists.
- Decide if you prefer to hire a consultant or a freelancer. Comparing data science consulting rates with a freelance data scientist salary.
- Post requirements on job boards and scan the boards for suitable candidates.
- Shortlist candidates based on their skill sets, experience, and requirements.
- Conduct technical tests.
- Interview face-to-face or video-conferencing interviews.
- Finalize the candidate that clears both technical test and interview rounds.
- Sign the contract to hire the data scientist or consultant.
Top 10 interview questions for data scientists
We have listed some questions you can ask a data scientist when you hire them. We ask a mix of technical, situational, and behavioral questions to assess their soft and technical skills.
1. Explain what regularization is and why it is useful.
Rationale: This question helps to assess their knowledge
Answer: Regularization is the process of adding a tuning parameter to any model. This helps to induce smoothness into the model to prevent overfitting. Regularization is done by adding a constant factor to an existing weight vector.
2. Which data scientist do you admire the most?
Rationale: A good data scientist should be capable of studying and adapting methods used by data scientists
Sample Answer: The below are my favorite list of data scientists,
- Geoff Hinton, Yann LeCun, and Yoshua Bengio – for persevering with Neural Nets
- Demis Hassobis for his work on Deep mind
- DJ Patil for using data science to make the government work better.
- Jake Porway from DataKind and Rayid Ghani for using data science for social good.
- Kirk D.Borne for his influence and leadership on social media.
3. How is logistic regression done?
Rationale: The questions test knowledge for an entry-level data scientist.
Sample Answer: Logistic regression measures the relationship between the dependent variable and one or more independent variables by estimating probability using the logistic function.
4. What is the difference between data scientist vs data analyst?
Rationale: Several professionals confuse the role especially when they begin.
Sample Answer: Data analysts study the data to identify trends. The analyst helps to decode the stories told by the number. They create data representation of the data. Data scientists on the other hand can not only create data but also create mathematic models. Another difference data analyst to a data scientist is that a data analyst deals with structured data while a data scientist deals with large quantities of structured data.
5. How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression.
Rationale: data scientists create data models to analyze data better.
Sample Answer: In multiple regression models, we have one dependent variable and several independent variables. You can predict the outcome of the dependent variable by using one or more independent variables. Usually, the R2 value will let you know if your model is correct. We can use the R2 value to test unseen data to check the validity of the model or improve the validity.
6. What is root cause analysis?
Rationale: It is a technical question to assess the data scientist’s knowledge.
Sample Answer: The root cause of 5 whys is the subject of quality analysis usually used in production or quality management. Under this approach, we ask several whys to understand the principal cause of any problem.
7. Are you familiar with pricing optimization, price elasticity, inventory management, competitive intelligence?.
Rationale: This question checks if the candidate knows how to utilize knowledge.
Sample Answer: Price optimization: Under given conditions or constraints on some variables like supply, cost of manufacturing, employees salary, etc. you try to set up a reward for a certain product that can help maximize profit. Price elasticity is the changes in demand when prices increase or decrease demand. Inventory management is optimizing how data so that you don’t store space or block cash flow.
8. What is false positive and false negatives?
Rationale: The data scientist os asked technical questions to determine technical skill.
Sample Answer: Depends on the problem at hand. False-positives are a wrong prediction of something absent when it is present or available. False-negative is a wrong prediction that something is present when it is absent.
9. What is selection bias? Why is it important and how can you solve it?
Rationale: These are some basic questions regarding statistics.
Sample Answer: Selection bias when data does not have random and have an inherent bias. Results obtained on a sample cannot be generalized for the entire population. It is important to take care of the bias and avoid it. We can avoid selection bias by taking a wider selection to make the distribution even.
10. How can you select K for K-means?
Rationale: It is a statistical question, statistics is used by data scientists to evaluate data.
Sample Answer: We use the elbow method to select K for k-means clustering. The idea of the elbow method is to run k-means clustering on the data set where K is the number of clusters. Within the sums of the square (WSS), it is defined as the sum of the squared distance between each member of the cluster and its centroid.
11. why should we hire you?
Rationale: A data scientist should be able to show his best qualities.
Sample Answer: I am a systematic and methodical worker. I am good with numbers, collecting data, and market research. I am adept at data visualization, you can pinpoint actual problems and find opportunities with the help of my data visuals. I enjoy become a partner in the growth of a company.
Top 10 tools to use while hiring a data scientist
There are several tools available that have made the process of hiring data scientist much smoother,
- Job description – A well-written job description will attract the best candidates. Recruiters specify the importance of creating an attractive job description. A good job description should be specific, short, and sweet, and include salary.
- Candidate sourcing tool – These are job boards, internet resources that are at your disposal to help you post job requirements and view candidate profiles. LinkedIn, Unremote, and Indeed are some popular candidate sourcing tools.
- Application tracking system – Application tracking systems help to filter through massive amounts of resumes quickly. Some popular ATS are ICIMS and SmartRecruiters.
- Recruitment assessment tool – Recruitment assessment tools help to conduct recruitment tests of the candidate. You can use Pymetrics.
- Mobility devices – Mobility tools help to engage with candidates on their smartphones, tablets, and other smart devices.
- Video conferencing tools – Video conferencing tools help you conduct face-to-face interviews remotely. Zoom, GoogleMeets are some of the video conferencing devices used,
- Chatbots – Chatbots and AI tools help to engage with potential recruits during off-hours, it enables virtual chats and collaborations.
- Candidate Relationship Management software – CRM is a tool used to increase engagement with recruits during the recruitment process. e.g. SmashFly.
- Background Checker – Background checkers check if the candidate has a criminal record and verifies the candidate’s work history. You can use Accurate Check.
- Onboarding tools – Onboarding tools help the candidate integrate into your organization faster. E.g. Vultus Connect.
Dos and don’ts while hiring a data scientist
Here are some dos and don’ts that will make your data hiring process more effective,
List of Dos,
- Articulate why you are hiring a data scientist.
- Conduct a fair and open recruitment process.
- Manage expectations of non-technical staff.
- Foster data-driven culture.
- Pay the data scientist according to market rates
- Calculate if hiring a free data scientist is more feasible than hiring a consultant. Use the data scientist hours approximation to base your calculations.
- Ensure the data scientist has the resources to work within your organization.
- Assigning the place where they fit in the organization.
- Assign a dedicated product manager or supporting staff.
- Do test relevant skills for the job.
List of Donts,
- Hurrying diving into advanced data in the initial stage
- Do not start a data mining program without an outlook on how to use the data
- Don’t try to hire the perfect data scientist that satisfies every criterion.
- Unprepared for the recruitment process.
- Don’t put technology before your business.
- Use unrealistic interview techniques and metrics.
- Making data scientist your first hire
- Hire a data scientist who refuses to sign a non-disclosure agreement.
- Hire someone who does not give references.
- Forget to treat the candidate politely whether you hire them or don’t.
How much does it cost to hire a data scientist?
Hiring a data scientist can be expensive. Typically data scientist hourly rate ranges from $35 to $200 per hour. The average salary data scientist average salary in the US was $117,345, and the average salary of a data scientist in the UK was £100,000.
The data analyst consultant’s hourly rate ranges from $38 to $75. The average data analyst salary per hour is $ 51.
When should I hire a data scientist?
There are several reasons why you would benefit from hiring a data scientist,
- Monetize your data – Retail sites can use data to customize the product that is shown on the website to increase sales.
- Mitigate the risk of your company – the data tasks allow you to analyze client patterns. They can notice changing customer trends. A data scientist can help you evaluate data from other businesses before you partner up with them.
- Gain a better understanding of your customers – Data scientists help you monitor changing customer preferences.
- Get assistance with business expansion – Data scientists will help you find new markets where you could expand.
- Improve forecasting – Data mining by using machine learning and neural network can help improve the predictions.
- Receive objective business decisions – Data is based on facts, data scientists help to present the facts and help you make objective decisions.