Top 10 things recruiters look for in Data Science aspirants

The Data Science ecosystem provides businesses with crucial means for data-driven decision-making. This ecosystem is now predicted to grow by roughly 28% through 2026. This shows that Data Science is one of the most demanding careers in today’s world. Businesses constantly compete with each other to get the best talent, who possess both technical and non-technical skills. Getting proficiency in skills sought by Data Science recruiters ensures that you have access to a rewarding career and continual growth.

This helps employers recruit, hire, and retain the best talent necessary to succeed in today’s competitive environment. To get the best Data Science job, aspirants should also possess certain skills and Data Science resume tips that recruiters highly consider. Besides helping you with interview preparation for Data Science roles, we will also examine the skills sought by Data Science recruiters in this guide.

Top things recruiters look for in data science aspirants

Building a successful career in Data Science necessitates expertise in tools, technologies, and non-technical skills. As a part of your working Data Science resume tips, having these skills in your resume helps you stand out in the recruiting process. Some of the industry-specific knowledge in Data Science you must be familiar with are.

Top things recruiters look for in data science aspirants

01. Technical proficiency

Python and R have always been in demand, but they play the utmost significance in Data Science. It is also a must for a data scientist to be proficient in SQL, as well. A data scientist ought to be an expert in pattern recognition, supervised and unsupervised learning, and data mining. Along with practical knowledge of data processing and visualization tools, they should also possess skills in statistical analysis and machine learning methods. Technical proficiency is among the most important qualifications for data science jobs, which every recruiter seeks.

Also Read: Rising demand for data science in India

02. Solid mathematical foundation

Robust data analysis and modeling are predicated on a solid understanding of statistical techniques, hypothesis testing, probability, and linear algebra. Proficiency in these mathematical topics enables data scientists to thoroughly examine problems from all aspects before formulating conclusions and hypotheses. A solid mathematical foundation is also important for efficiently analyzing and interpreting massive amounts of data, getting insightful knowledge, and creating models and solutions for challenging issues.

03. Business acumen

A Data Scientist’s ability to effectively integrate data science projects with business goals is one of the most important skills sought by Data Science recruiters. To align a project with business needs, data scientists need to be aware of the opportunities and problems unique to their industry. They have to know when to seize chances and look for answers to all issues. The secret to creating productive working relationships is to communicate politely and clearly. Thus, data scientists should have strong communication abilities and solid business understanding.

Interesting pick: How to build a robust data science portfolio?

04. Hands-on experience and projects

Your practical experience can improve your understanding of your abilities and capabilities as well as, most significantly, your exposure to real-world situations. Furthermore, to completely understand the professional issues in a Data Science job, practical experience is required. Developing a career in Data Science is always more about how well you can apply your practical abilities to find a solution than just finding a problem. For this reason, gaining a lot of practical experience is essential to a successful career in data science.

05. Continuous learning and adaptability

The primary role of data scientists is to find solutions to unique problems pertinent to the industry. Old methods, however, are not inevitably useful for solving new challenges. Data scientists may close the gaps and consistently generate trustworthy analytical results by continuing their learning. This will help them better prepare for shifting business and market dynamics. Data scientists should, therefore, be dedicated to keeping up with industry trends and open to learning new tools, languages, and techniques.

06. Problem-solving and critical thinking

Critical thinking and problem-solving skills are important qualifications for Data science jobs. It helps the data scientist to provide the capacity to analyze complicated problems logically and use data-driven techniques to suggest workable solutions. Professionals with a reliable problem-solving approach are better equipped to find solutions. Proficient critical thinkers can also separate important facts from less important elements to solve problems or come to the right choices from a set of data.

Also Read: Types of resumes for data science aspirants

07. Effective communication

To make sure that the approach taken to solve a particular issue is the best one, a skilled data scientist interacts with a variety of individuals within an organization. To effectively connect with marketing teams, corporate leaders, and development teams, a data scientist must comprehend and communicate software specifications as well as business requirements. Data scientists who can communicate technical solutions to non-technical teams with clarity are in high demand by organizations as they contribute to the smooth process of business operations.

08. Collaborative team player

Working in a team is crucial to the field of data science, and one of the most vital skills is learning to listen to people and value their opinions. Working together can boost productivity, reduce errors, and enhance the standard of work. For this reason, recruiters place a high value on data scientists who can cooperate with data engineers, business analysts, and other stakeholders, have experience working in cross-functional teams, and can foster a culture of positivity and knowledge exchange within the team.

09. Passion for data science

A data scientist’s level of passion can be determined by their interest that goes beyond professional or academic qualifications. A valuable data scientist is also dedicated and eager to learn new tools and skills. A data scientist’s passion opens doors for valuable data insights. Engaging in data science forums, conferences, workshops, individual projects, or research projects demonstrates passion and dedication.

Also Read: What are the commonly asked data scientist interview questions?

10. Educational background and certifications

Gaining the necessary abilities through appropriate degrees and academic credentials is the key to moving from an entry-level data job to a six-figure data professional. Aspirants can upskill more quickly by being familiar with the newest technologies and approaches in data science with the support of renowned certifications from reputable organizations or platforms. Hiring managers value applicants who emphasize ongoing certifications by attending essential workshops and courses to stay current on industry trends. 

How do MAHE’s data science and business analytics programs help complete your career dreams?

Poor data quality causes worldwide firms to lose 20–35% of their revenue every year. That is why global businesses are on the lookout to find and hire professionals with the right qualifications for Data Science jobs. At MAHE (Manipal Academy of Higher Education), we are dedicated to offering the comprehensive Master of Science in Data Science and Business Analytics program that helps you develop proficiency and expertise in Data Science.

The program helps you succeed in the area by giving you the tools you need to forge a remarkable career in leadership and analytical positions in a variety of industries. Here is why you must pursue an MSc in Data Science and Business Analytics with MAHE:

• Hands-on experience and practical exposure
• Get expertise in industry-relevant tools and strategies
• Learn practical applications through industry-focused models and projects
• Learn from experts with decades of expertise in data science and business analytics
• Strong emphasis on overall personality and soft skills development

Also Read: How to create a strong data scientist resume

Conclusion

Data Scientist roles are predicted to rise 35% faster than average during 2022-32. This shows that the number of jobs in data science is increasing in a number of industries. Industry-specific knowledge in Data Science can help you get a high-paying job in the field. This necessitates considering working Data Science resume tips and top qualities recruiters seek in candidates.

Select MAHE as your mentor in growth and our MSC in Data Science program as the path that leads you to it. The program helps you create a lucrative, successful, and sustainable career. You can also master diverse skill sets that drive you toward success and proficiency in the dynamic field of data science.