Everything you need to know about Data Science recruitment

What is Data Science is a question that has plagued many in the last two decades. But the answer couldn't be simpler.
Data Science reveals trends and insights businesses can use to make better decisions and develop more innovative products and services. Well, not just businesses, but its extracted value extends beyond businesses and into academic and social pursuits. There is virtually, and arguably, no industry that can't benefit from it.

iMocha Hiring Trends Report 2022

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Data Science in the IT industry has made its mark, but industries such as retail and e-commerce, logistics and transportation, healthcare, finance, insurance, and real estate have tons of data that needs analysis. A robust data science team working in these industries can truly leverage the data within their organization to gain a competitive advantage—one of the reasons why it is one of the most rewarding careers today.

Data science has a substantial impact on any business decision-making process, and all companies, at some point, will be looking for intelligent, actionable insights from their data to become profitable and optimize their operations further.

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The state of Data Science industry

While the term 'Data Scientist' came into existence around 2008, the industry started gaining momentum in 2010. It was coined by D.J. Patil and Jeff Hammerbacher, then the respective leads of data and analytics efforts at LinkedIn and Facebook. In 2012, Harvard Business Review declared data scientists the 'sexiest' job of the 21st century. Professionals started rooting for data science as the field of work right after; even the demand for data scientists amongst organizations skyrocketed after this declaration.

Today in 2020, it is still believed to be the up-and-coming domain, and data science applications are not limited. In fact, according to a report seeking inputs on developer skills, data scientists make up only 2% of the tech talent pool. But more than 1 in 4 may be looking for work right now. These numbers have been rising since the Covid-19 pandemic.
Glassdoor listed data scientist as the #1 job in America in 2020, making it valuable and popular in all industries.

According to another report on developer skills, data science talent is most concentrated in the United States at 30.1%, followed by India at 23.7%, Brazil at 5.4%, and the UK at 2.7%. But they're also well distributed across Europe, the Middle East, and Asia. Likewise, the demand for data science skills is the most in the United States, followed by Europe, the UK, Canada, China, and India, with the hiring industries being IT, e-commerce, BFSI, healthcare, retail, and manufacturing.

Primary data science applications in an organization

BI for making smarter decisions:

One can analyze data at a large scale and derive meaningful insights to facilitate more intelligent decision-making strategies.

Enhancement of products:

Performing analysis of customer reviews, current market trends, size, and demographics analysis to suggest improvements in the existing products can be easily made using data science methodologies.

Managing business efficiently:

Data scientists analyze the companies' health and predict their strategies' success rate and identify critical business metrics that are essential for determining business performance. Based on this, the businesses take important initiatives to quantify and evaluate their performance and take appropriate management steps.

Better predicting outcomes:

Predictive analytics is highly applicable in customer segmentation, risk assessment, sales forecasting, and market analysis. Despite the industry, predictive analytics can predict future events and results aligned to those.

Leveraging data for business decisions:

You can make faster and more accurate data-driven business decisions and reduce the chances of failure. Meanwhile, finding correlations between age and income can help the company create new promotions or offers for groups that may not have been accessible. A robust data science team in any organization adds value to almost all company functions, such as marketing, HR, finance, training, and operations. Data analysis can lead to better decisions that allow organizations to grow in smart, strategic, and profitable ways.

Assessing business decisions:

Post implementing various business decisions, companies need to analyze their performance and growth. Data Science helps them analyze it and eliminate the problem that slows down their performance.

Automating recruitment process:

Data science technologies such as Image Recognition converts visual information from resumes into digital formats. It then processes the data using various algorithms like clustering and classification to point out the right candidate for the job.

Training staff:

In any company, keeping the team informed and up-to-date can be a difficult task. Data science pulls insights that the employees need to know and populates them through online knowledge-based software or IT documentation software.

Find the right target audience:

Every piece of data that companies collect from customers – whether it be social media engagement, website visits, or email surveys – contains data that can be analyzed to understand the customers more effectively. Using data science with the information the customer provides, companies can combine data points to generate insights into the target audience more effectively. It allows companies to tailor the company's services and products to particular groups.

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Most popular data science roles

Data science roles and their function are relatively new in the market. The primary data science job titles are Data Scientist, Data Analyst, Data Engineer, and Data Architect. The common thread in these roles is the love for mathematics, statistics, physics, psychology, and most importantly, coding. We've tried to summarize these data roles and responsibilities so you know what to expect from each role:

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Data Scientist

Data scientist roles and responsibilities include using machine models to solve challenging problems in all business areas. These professionals have mastered using Natural Language Processing to mine unstructured data and extract actionable insights. They significantly work on structured data with advanced statistical methods and algorithms to perform analyses. They interpret the results and visualize the data to convey the best action points to the management and stakeholders to achieve their business goals.

Data Scientist is the highest paying job profile in the data science function with the highest education and experience requirements. Today, most data scientists are majors in mathematics, applied statistics, operations research, computer science, physics, and aerospace engineering.

Data Analyst

A Data Analyst generally has to shuffle between strategic and operational initiatives. They extract data, analyze it, and convey data-driven insights to decision-makers. The other two critical areas of work in this job role are developing predictive analytics models to support business initiatives and managing risk and compliance data to make it more understandable.

The seniority at which Data Analysts are placed varies from the -skillset and the experience they possess. But, to sum it up, the experience of working on real-world problems, exposure to advanced software programs, and knowledge sharing with experts will likely put professionals on the data analyst track.

Data Engineer

Data Engineers are the people who ensure the data is clean, organized, and ready for analysis. They are the ones who lead big data initiatives — the large scale and complex ones. They collect, manage, analyze, and visualize large datasets and turn them into actionable insights using various techniques, toolsets, and cloud platforms. All that overwhelming data truly gets its shape at the hands of these data engineers.

Professionals looking to work in data science usually turn to Data Engineering as their common choice. It is said to be the profile that guarantees success for data science professionals in the future.

Data Architect

Data Architects are analytical and creative minds and technical experts who adapt data management and storage strategy. They create the database from zero; they design how data is retrieved, processed, and consumed. They also control access to the data and continually improve how it is collected and stored. They continuously innovate ways to enhance data and reporting quality, reduce redundancies, and offer better data collection sources, methods, and tools.

A few other data science roles are BI Analyst, Database Administrator, Machine Learning Engineer, Statistician, and Data and Analytics Manager. More and more professionals from all over the world are entering this new field every day.

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Skills required for the most popular job roles in data science

Data Science skill set is varied. People from several functions are employed in this industry, so it is necessary to jot down some common skills required for each title.

Here's a data science skills checklist for you to follow:

Data Scientist
  • Programming skills in R, MatLab, SQL, Python, SAS, SCALA, and other complementary technologies
  • Familiarity with BI tools, e.g., Tableau
  • Mathematical skills such as statistics & algebra
  • Experience with big data technologies such as Hadoop and Spark
  • Data storytelling skills
  • Ideally, a strong background in engineering, computer science, machine learning, statistics, or applied mathematics.
Data Engineer
  • Programming skills in Java, Scala, Python, SQL, and Machine Learning
  • NoSQL databases such as MongoDB, Cassandra DB
  • Experience with frameworks such as Apache Hadoop
  • Experience in statistical modeling and regression analysis
  • Ideally, a degree in software engineering, computer science, or information technology
  • Additional vendor-specific certification offered by Google, IBM, Cloudera, or Microsoft Certified Solutions Expert
Data Analyst
  • Programming skills in R or Python, SAS
  • Familiarity with BI tools, e.g., Tableau
  • Experience with statistical software, e.g., Stata, SPSS
  • Proficiency in Microsoft Excel and the ability to use advanced analytics and formulas
  • Data wrangling, Data warehousing, and Data visualization skills
  • Ideally, a Bachelor's degree in IT, computer science, or statistics.
Data Architect
  • Programming skills in Python or R, SQL
  • Knowledge of C, PHP, and JAVA languages
  • Familiarity with BI tools, e.g., Tableau
  • Experience in data modeling and machine learning
  • Ideally, a bachelor's degree in systems, computer science, engineering, or related fields.

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How can recruiters build a hiring pipeline for data science professionals?

Data science hiring is a reasonably tricky task as hiring for it without understanding the skills, tools, and technical expertise they possess will lengthen the process. Not just theoretical but practical experience of the tools, but the ability to build solutions and real-world use cases matter the most when hiring data scientists. Additionally, with not much formal education available, some professionals might call themselves data scientists without proper credentials, quickly becoming a grave challenge recruiter face these days.

Seth Dobrin, who heads IBM's Data Science Elite Team, has an excellent suggestion for recruiters. He suggests that if a company is building a data science team, the 1st step is to hire a Senior Data Scientist who can further lead to the team's development.

As the industry is still quite a niche, until senior professionals are on-board, it isn't easy to get others to come on board. Two years ago, Dobrin was hired to build out the Data Science Elite Team. In this new endeavor, IBM data scientists engage with organizations in six to 12-week engagements to collaborate on data science and AI projects. After spending a year traveling while meeting IBM clients, he successfully built a team of 60 data scientists, machine learning experts, and others with related expertise. Not just that, in 2019, he added 30 more data scientists to his team.

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When hiring data scientists, large job directories, such as Glassdoor, Indeed, and LinkedIn, are popular and often the first choice for companies. Hiring data scientists typically includes applications, pre-screening, technical tests, in-person or virtual interviews, and selection. This can be a successful method; however, large tech companies avoid listing their job offers on these websites for fear of getting too many applications. It is often difficult to find the right fit from a haystack.

Besides these, hiring data scientists through peer networks and external consultants is a good source. Given the talent pool is a niche, the employees might refer to friends, professional contacts, and acquaintances they know would fit a particular role. The field's nature is more research-oriented and unsaturated, so there is a high chance that professionals from this field are well-connected.

Some smart ways of recruiting data scientists are also through non-traditional methods such as Hackathons, GitHub, Conferences, WhatsApp and Telegram Communities, and Local Meet-ups. You'll find data science interview questions in the fifth section of the paper.

How can recruiters build a hiring pipeline for data science professionals?

"In a competitive field like data science, strong candidates often receive three or more offers, so the success rates of hiring are typically below 50%. There is more than one way to source data science professionals; however, below are the three communities that stand out in efforts and outcomes."

-Firstround.com

  • Hackathons:

Hackathons have become one of the popular methods in the analytics community to hire the right fit. Big and small, many companies are partnering with hackathon platforms to spot data science candidates. It is one of the top-rated platforms to demonstrate skills while competing with the best programmers in the domain.
They are a 24-48 hours event that provides an innovative and energetic environment where participants use different tools to analyze, visualize the outcome, and win the code race. Recently, many organizations have started collaborating and organizing hackathons to identify and gain new talent. Some also offer practice sessions where data science enthusiasts usually practice Machine Learning algorithms like Support Vector Machine (SVM), Linear Regression, Naive Bayes, Extreme Gradient Boosting Classification, and more.

Hackathons are one of the best mediums for sourcing data science candidates because they:

  • Reduce time to shortlist on-the-spot testing of hard-skills
  • Identify skills that are out of comfort zone
  • More fruitful than the traditional hiring process
  • Filter the best candidates from the talent pool

  • GitHub:

GitHub is one of the world's largest code hosts, with nearly 50 million developers. It is a perfect platform to showcase work by machine learning and data science enthusiasts. The platform allows team members to collaborate to showcase coding skills while acting as an online resume. It is becoming a revolutionary platform for identifying data scientists and their skills. Data science professionals use GitHub to host code repositories, data, and interactive explorations, present their work, and impress hiring managers. The job aspirants usually set up an account on GitHub to create a work repository. The platform allows team members to collaborate to showcase coding skills while acting as an online resume. It is becoming a revolutionary platform for identifying data scientists and their skills.

  • StackOverflow:

StackOverflow is a Q&A site for professional and enthusiast programmers. Like GitHub, StackOverflow is also an excellent platform for hiring exceptional data science talent. It is a Q&A site where developers post and answer technical questions. Tech recruiters must carefully read the candidates' answers addressing specific questions to see if they are the right fit.

On the StackOverflow platform, the developers are segregated based on their user badges and reputation scores. An ideal candidate ranks high for both, which should be easier for recruiters to gauge. Every question posted has associated tags; they can be used to find users who fit the company's data science requirements. However, after connecting with a candidate, it is essential to validate the resume and conduct a tech skills assessment to shortlist him/her for the next round of interviews.

Another way to find excellent data science talent is through machine learning challenges, similar to the hackathons we mentioned above. The coding challenges are great platforms for candidates to showcase their skills. While hiring top Data Science talent, testing candidates on real-time problem-solving skills can increase recruitment efforts by days or weeks.

Are you hiring an entry-level data science professional or an experienced one?

Companies that do not have a reliable data infrastructure and internal BI practice need a data engineer first. S/he will build pipelines and prepare data for the data scientist to use. Many companies skip this step because it's not the mainstream data science profile, but that is a mistake. If a data scientist is hired first, they won't have any data to work on, so they will either leave or deny working as a data engineer, as crunching data from scratch isn't something a data scientist does in his profile. Hence, companies trying to establish a new data science team must hire a data engineer before a data scientist.

As mentioned in the section on "Building a hiring pipeline for Data Scientists," the companies need to hire a senior data scientist. Cutting costs or settling for lesser experienced data scientists won't help the company with problem-solving skills. They will move quickly with minimal assistance, giving the company a faster return on data science investment. Senior candidates usually command a higher salary than entry-level candidates, but they are typically a revenue addition to the company. Hence, it's rather imperative that someone experienced steers the ship to the shore.

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Hackathons and platforms for hiring the best Data Science professionals

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The recruiters can benefit from a comprehensive list of the 12 best platforms becoming popular data science hiring places to go. They fall under non-traditional, unconventional hiring techniques for data scientists. As seen below,

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Data Science interview questions to ask during the interview

         Technical interview questions:            
  1. What is the curse of dimensionality, and how should one deal with it when building machine-learning models?
  2. Why is a comma a wrong record separator/delimiter?
  3. How do you determine "k" for k-means clustering? Or how do you choose the number of clusters in a data set?
  4. What's more important: the predictive power or interpretability of a model?
  5. Explain finite precision. Why is finite precision a problem in machine learning?
Practical Experience Interview Questions:
  1. Describe a recent use of logistic regression.
  2. Describe an analysis you have recently completed, including strategies and findings. How did the business use the findings?
  3. Give examples of data-cleaning techniques you have used in the past.
  4. What topics/tools would you include in a one-day data science crash course? And why?
  5. Describe a situation where you had to decide between two different types of machine learning algorithms, and why you chose the one you did.

If you are interested in more Data Science interview questions, here are 100 categorized interview questions with answers.

Pitfalls to avoid while hiring data science professionals

Incorrect job title

The right job title and knowing what the company wants in hiring is a job half done, but an incorrect one can lead to a talent that doesn't match the requirements. We see how companies often use "Data Scientist" as a title, but they need to differentiate what they are looking for. Does your company need someone to build analytics dashboards and track critical metrics? Or to create prediction algorithms? or develop your data ingestion workflow? Also, there is a vast difference between an ML Engineer, a Big Data Developer, a BI Analyst, and so on. It is necessary to realize the requirements and define the right job title, to save time and effort in finding the right talent.

Not emphasizing on interesting problems

Often, recruiters follow a natural path of telling data scientists about benefits and pay, leave policies, and recruitment processes. But data scientists love problem-solving; they generally move from one company to another as they have a hunger for solving real-world problems and scenarios.

Hence, it is a good idea to speak about business problems in brief. It will also help if recruiters can use industry terminologies and talk about technical skills and toolsets. The job needs to come across as an opportunity to learn and work together on technological advancements.

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False expectations of experience

Data scientist job profiles are just a few years old. When companies or recruiters are looking for a Senior Data Scientist, they often expect the data scientist to have a few years of experience. While it's natural in other industries, it can't be right for data science. Many researchers and analytics or statistics professionals have been doing data science every day without being labeled as one.

Traditionally sourcing strategies to hire

Qualified data scientists are in high demand and short supply. A well-thought-out sourcing strategy that will attract the right talent pool is essential. Companies can't expect data science professionals to jump ship only based on the job descriptions or a few calls with the company. It is crucial to create brand awareness about your company in the data-tech community. A niche industry such as this works on the connections, and it makes sense to go beyond the traditional hiring strategies and establish your company as a thought leader in the field.

It can be achieved by speaking at conferences or exhibiting at various conferences or events, or participating in webinars. Initiatives like these attract talent and intrigue them to consider open opportunities for the companies.

Approaching interviews with traditional processes
Like hackathons or coding challenges, companies can make the interview process more relevant to the industry. Many research reports show that interviewing alone is not reliable for validating candidates' skills and often causes both candidates and employers. Instead, it will make sense to create a data science challenge in the skills assessment that all applicants need to participate in. The challenge can pose a real business problem that stimulates a day in the life of the job. The result is a valid comparison across candidates and their skill sets that bring out the best and right fit talent of the lot to work with your company.
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