Key Attributes Employers Seek in Potential Data Scientist Applicants
In the dynamic world of data science, technical prowess is no longer the sole ticket to success. A forward-thinking individual, recently promoted to a data science manager, emphasises the importance of developing non-technical skills to excel in the field.
The individual encourages aspiring data scientists to seek cross-functional opportunities, collaborating with as many people as possible to build up the ability to manage stakeholders effectively. They also recognise the prevalence of "imposter syndrome" among less-tenured data scientists and encourage them to speak up when they see something inefficient, proposing solutions to improve processes.
One of the key non-technical skills the individual prioritises in candidates is the ability to learn. This curiosity and lifelong learning attitude is crucial for adapting to the ever-evolving landscape of data science. To test this, the individual asks candidates about past situations where they lacked a necessary skill and how they dealt with it.
Communication is another vital skill, with the ability to explain complex data insights to stakeholders who may lack technical expertise being critical. The individual recommends practising explaining technical concepts in simple terms, either through presentations or writing, to develop this skill.
Problem-solving, collaboration, business acumen, attention to detail, time management, adaptability, empathy, critical thinking, and strong analytical and critical thinking skills are other non-technical competencies the individual values in candidates.
To cultivate these skills, the individual suggests engaging in real-world projects or internships that require cross-team collaboration and stakeholder communication, developing business knowledge by studying industry trends, and working on time management techniques. Building problem-solving skills can be achieved by regularly tackling varied analytical problems, while adaptability can be fostered by learning new tools and programming languages regularly.
The individual also recommends soliciting honest feedback from stakeholders to learn how to become "great to work with." They also test candidates' business acumen by constructing business cases for them to work through.
The individual's promotion to data science manager was due in part to the great work of the two best data scientists they have ever hired in 2021. They advise against taking anything as a given and questioning things that seem inefficient to foster the ability to identify inefficiencies and propose solutions.
In the new year, the individual will be the official hiring manager for their team. They believe building business acumen can be achieved by learning about others' work in the company, familiarising oneself with the company's strategy, and staying up to date on industry news.
In conclusion, aspiring data scientists should deliberately blend technical skill development with focused efforts to strengthen these essential non-technical competencies to succeed in their careers.
The individual encourages aspiring data scientists to engage in education-and-self-development, particularly in business and technology, to build up the ability to understand the broader context and business objectives of the data they analyze. They also recommend taking up programming languages and tools related to technology to enhance problem-solving skills.
Furthermore, the individual emphasises the importance of finance in making informed decisions about resource allocation within a business. Therefore, they advise aspiring data scientists to have a strong understanding of financial concepts and principles to guide their analysis and provides opportunities for candidates to demonstrate their financial acumen within the hiring process.
