Build Winning Teams with iMocha

Company News, Diversity & Inclusion, iMocha Engineering Product Updates Remote Hiring Skills Assessment

All Posts

Data Science is booming and getting in demand with every passing day. We have already discussed this in our previous blog, i.e., The Rise And Rise Of Data Science! This significant boom in the data science industry came because organizations today are making a shift from CRUD (Create, Retrieve, Update, Delete) systems to systems of insight. Thus, traditional ways of data processing are no longer suitable for processing a huge amount of data (big data) in fields like:

  • Customer Segmentation
  • Weather Prediction
  • Medical Diagnosis
  • Face & Voice Recognition
  • Language Processing
  • Sentiment Analysis
  • Recommendation Engines

In today’s time more and more companies are looking for data scientists to mine their data and put it into use, however, how to hire a data scientist still stands as a challenge. Therefore, we’ve jotted down a list of challenges faced while recruiting data scientists.

3 Data Science Hiring Challenges

1. Job Description (JD) Lacks Accuracy

When recruiters want to hire a data scientist they make the very first mistake while preparing a JD, to be precise in the “Job Title.” They use the word “Data Scientist,” I should rather say misuse it a lot more than needed. It’s like a catch-all title for them.

Let’s say you are hiring for a machine learning engineer even though the job role depends on the knowledge of data science, a machine learning engineer would be poles apart from a data analyst. A job title that is generic may hint otherwise than what you are actually looking for, triggering a lot of responses that aren’t going to be useful.

You should also mention the kind of problem you want to get solved in the JD! The best data scientists are attracted to the problem. This basically means emphasize the intellectual aspects of the job. It will prove beneficial for the data scientist recruitment managers as most resumes would be of professionals interested in the problem emphasized in the job description.

2. Narrowing Down The Job Title 

The term “Data Scientist” was coined 11-12 years ago and as a profession, it has only grown since then. It has become a more mainstream job over the years. However, when recruiters post a job role for a data scientist, they set their eyes on someone whose designation says, data scientist.

This is an absolute blunder as professionals in the field of quantitative finance, bio-statistics, and high energy physics, also work on data science but don’t use the title “data scientist.” There is a huge population of such underutilized manpower in the industry.

Therefore, make sure as a recruiter you do your research well and be open to resumes of candidates who handle data work.

3. Assessing Issues

It is pretty evident that just the interview of a candidate can never validate his/her skills mentioned in the resume. Therefore, the best way to judge the candidate’s skills is by sending them a data science test

A test that contains real business problems that take place in the organization on a day-to-day basis. The candidate’s submissions should be scored consistently and quantitatively. The test should prove to be a source to funnel the best and high-performing candidates to the interview. The efforts you put into selecting a candidate resonates with the kind of organization you are and how rewarding it would be to work at your company.

Also Read: Data Science Interview Questions

Importance Of A Data Science In An Organization

We have already discussed in our previous blog, i.e., The Rise And Rise of Data Science, how tycoons like Amazon use data science to retain and enhance the customer experience.

Therefore, if any organization or person questions the importance of data science, the answer to them would be-it solves your business problems. Most of the time the organizations want the data scientist to handle big data science projects without realizing its purpose. The management should always present the data scientist with a problem and let them create the solutions. You can’t expect them to come up with an ML project without knowing the end goal.

Uses of Data Science -

  • Finds out what triggers the churning of customers and hence improves customer retention.
  • Improves the internal process by figuring out points where the fault lies.
  • Targets customers at the right time with the right messages.
  • Shows insights on how people use your products, therefore, help in product development.
  • Analyzes customer sentiment on social media.
  • It also helps in financial modeling, i.e., building a model of a real-world financial situation.

Also Read: 53+ Categorized Data Science Interview Questions For Technical Recruiters & Hiring Managers

Job Roles And Skills Required

1. Data Analyst

A data analyst focuses on the analysis and solving of the problems related to data, types of data, and the relationship between different data elements within a business or IT system.

Skills Required

  • Documenting in detail and structuring business data/logical modeling.
  • Mining and analyzing data to understand the correlation among various data points.
  • Mapping and tracing data from system to system.
  • Design and create data reports
  • Perform statistical analysis of data

2. Data Engineers

A data engineer’s primary job involves preparing data for analytical or operational uses. The tasks can vary from organization to organization, however, it includes building data pipelines to pull together information from different source systems; integrating, consolidating, and cleansing data; and structuring it for use in individual analytics applications.

Skills Required

  • Database architecture and data warehousing.
  • Data modeling and mining.
  • Statistical modeling and regression analysis.
  • Proficiency in languages, especially R, SAS, Python, C/C++, Ruby Perl, Java, and MatLab.
  • Database solution languages, especially SQL, as well as Cassandra, and Bigtable.
  • Hadoop-based analytics, such as HBase, Hive, Pig, and MapReduce.
  • Operating systems, especially UNIX, Linux, and Solaris.
  • Machine learning, including AForge.NET and Scikit-learn.

3. Machine learning Engineer

ML engineers are programmers that create programs that enable machines and systems to learn and apply knowledge without any specific direction.

Skills Required

  •  Computer Science Fundamentals and Programming
  •  Probability and Statistics
  •  Data Modeling and Evaluation
  •  Applying Machine Learning Algorithms and Libraries
  •  Software Engineering and System Design

4. Data  Scientist

A data scientist analyzes and interprets complex digital data, like the usage statistics of a website, to assist a business in its decision-making.

Skills Required

  • Programming
  • Statistics
  • Machine Learning
  • Linear Algebra and Calculus
  • Data Visualization
  • Communication
  • Data Wrangling
  • Software Engineering
  • Data Intuition

5. Data Architect

A data architect is a practitioner of data architecture, a data management discipline concerned with designing, creating, deploying, and managing an organization's data architecture.

Skills Required

  • Applied math and statistics
  • Data visualization and data migration
  • RDMSs (relational database management systems) or foundational database skills
  • Database management system software, especially Microsoft SQL Server 
  • Databases such as NoSQL and cloud computing 
  • Hadoop technologies, like MapReduce, Hive, and Pig
  • Information management and data processing on multiple platforms
  • Machine learning
  • Data mining and modeling tools, especially ERWin, Enterprise Architect, and Visio
  • Programming languages, especially Python and Java, as well as C/C++ and Perl
  • Operating systems, including UNIX, Linux, Solaris, and MS-Windows
  • Application server software, especially Oracle
  • Backup/archival software

6. Business Analyst

A business analyst (BA) analyzes an organization’s business domain and documents its business or processes or systems, assessing the business model or its integration with technology.

Skills Required

  • The CS fundamentals
  • Programming in R and Python
  • Being adept with
  • The platform of Apache Spark.
  • NoSQL/Hadoop.
  • SQL Coding of its Databases.
  • Data visualization techniques.
  • Handling of unstructured data.
  • Proficiency in ML algorithms and AI.

7. Database Administrator

A database administrator is a specialized computer systems administrator who maintains a successful database environment by directing or performing all related activities to keep the data secure.

Skills Required

  • Knowledge of database queries
  • Knowledge of database theory
  • Knowledge of database design
  • Knowledge about the RDBMS itself, e.g. Microsoft SQL Server or MySQL
  • Knowledge of structured query language (SQL), e.g. SQL/PSM or Transact-SQL
  • General understanding of distributed computing architectures, e.g. Client-server model
  • General understanding of the operating system, e.g. Windows or Linux
  • General understanding of storage technologies and networking
  • General understanding of routine maintenance, recovery, and handling failover of a database

8. Statistician

Statistical analysis is the process of generating statistics from stored data and analyzing the results to deduce or infer meaning about the underlying dataset or the reality that it attempts to describe.

Skills Required

  • Mathematical ability and computer literacy
  • A clear understanding of statistical terms and concepts
  • Analytical skills
  • Written and oral communication skills
  • Problem-solving skills
  • Communicate results and findings to non-statisticians

9. Data and Analytics Manager

They provide direction to the team of data analysts. They also decide as per their experience where each analyst’s skills will help improve the organization’s productivity. They also oversee the analytics department making sure the reports generated are accurate.

Skills Required

  • Strong programming skills with querying languages: SLQ, SAS, etc.
  • Experience with big data tools: Teradata, Aster, Hadoop, etc.
  • Experience with testing tools such as Adobe Test & Target
  • Experience with data visualization tools: Tableau, Raw, chart.js, etc.
  • Experience with Adobe Analytics and other analytics tools
  • C, C++, JAVA, or other programming languages
  • Experience with Excel, Word, and PowerPoint.

Hiring data scientist can be hard, however, if the recruiter comes up with the right strategy they’ll be able to find an ideal candidate.

Subscribe to iMocha blog

Tanvi Sharma
Tanvi Sharma
Tanvi Sharma is a Content Strategist at iMocha. A seasoned marketer and branding consultant, she likes sewing stories together to help brands find their true and unique voice. A perfection enthusiast, she believes each and every word should serve a purpose while writing. When she’s not writing for work, she is writing fan fictions and theories, and volunteering at local animal shelters.
Find me on:

Topics: Tech Recruitment, Remote Hiring, Skills Assessment

Related Posts

Assess and Upskill a React.js Developer using Project Based Assessment on Real Life Scenario

JavaScript has been the most popular programming language for the past eight years, according to StackOverflow 2021. Not only that but JavaScript is used by 96.7 percent of the world's 1.9 billion+ websites.

4 Reasons Why Your Remote Recruitment Process Should be Revised

The remote recruitment process is all about showing trust in your resources and keeping the process simplified for them. 

50+ Machine Learning Interview Questions And Answers

A rigorous interview procedure is required for a Machine Learning interview, in which candidates are judged on numerous criteria such as technical and programming skills, method understanding, and clarity of basic concepts. If you want to apply for machine learning positions, you should be aware of the types of Machine Learning interview questions that recruiters and hiring managers are likely to ask.