By Saeed Mirshekari
September 12, 2024
The Three Verticals to Evaluate When Hiring a Data Scientist
Hiring a data scientist can be a complex task due to the multi-disciplinary nature of the role. Data scientists are expected to possess a wide range of skills, work well within teams, and have the capacity to grow and adapt in a fast-evolving field. To ensure the right hire, it is essential to evaluate candidates across three key verticals:
- Can they get the job done? – Skills
- Are they easy to work with? – Behavioral aspects
- Do they have potential for growth? – Potential
These three dimensions help assess not only a candidate's current ability to perform the role but also their fit within the team and their future potential to grow alongside the organization. Let’s break down each of these aspects and see how they can be evaluated during the hiring process.
1. Can They Get the Job Done? (Skills)
The most straightforward and critical vertical in hiring a data scientist is determining whether they have the necessary skills to complete the job successfully. A data scientist’s job is multifaceted, typically involving expertise in areas such as programming, statistics, machine learning, data engineering, and domain-specific knowledge. This vertical asks the question: Can they deliver results?
Key Areas to Evaluate:
a. Technical Skills
The technical competence of a data scientist is non-negotiable. Core skills include programming (Python, R, SQL), familiarity with machine learning frameworks (TensorFlow, PyTorch, Scikit-learn), and proficiency in statistics and mathematics. Candidates must demonstrate expertise in building predictive models, working with large datasets, and optimizing algorithms for real-world applications.
Evaluation during resume review and technical interviews is vital here. Resume keywords (such as "random forests," "neural networks," or "A/B testing") can give initial clues about the candidate’s experience. However, technical interviews or coding challenges are more reliable for assessing whether the candidate has practical, hands-on expertise.
b. Critical Thinking
A successful data scientist must also exhibit sharp problem-solving and critical thinking skills. Can they decompose complex problems and arrive at creative solutions? Do they demonstrate an understanding of when and how to apply various techniques?
Critical thinking can be evaluated during the interview process by giving candidates real-world problem scenarios to work through. Candidates who can critically assess the trade-offs between different models or approaches demonstrate the analytical thinking required for high-quality data science work.
c. Experience
Work experience, both in years and in relevance, is essential in this vertical. A candidate with experience working on similar problems is often more reliable, as they bring practical insights and lessons learned from real-world challenges. Past experiences indicate their ability to complete tasks efficiently, as well as their knowledge of the industry and domain-specific data science applications.
d. Education and Intelligence
While education alone does not guarantee success, it plays a role in shaping a candidate’s foundational understanding of critical topics. A strong academic background in quantitative disciplines (like computer science, statistics, mathematics, or physics) can indicate that a candidate has the ability to learn and apply sophisticated techniques.
A balance between formal education and practical experience is ideal. Some candidates may compensate for a lack of formal education with strong hands-on experience, a fact that should be considered during evaluation.
Stage of Evaluation: Resume Review
Many of these skill-based factors can begin to be assessed at the candidate review stage by looking at their resume and portfolio. A thorough review of their past roles, education, and projects can offer insights into whether they have the technical ability to complete the job. A technical phone screen or coding challenge can further confirm these skills.
2. Are They Easy to Work With? (Behavioral Aspects)
Technical expertise is only one side of the coin; a successful data scientist must also fit within the organization’s culture and work well with others. Collaboration is key in data science, as it often requires working cross-functionally with product managers, engineers, and business leaders. This vertical evaluates the behavioral aspects, or more simply: Can we work well with this person?
Key Areas to Evaluate:
a. Communication Skills
Clear and effective communication is one of the most important non-technical skills for a data scientist. They must be able to convey complex technical concepts to non-technical stakeholders, such as executives or clients. A candidate who can explain a machine learning model in plain language shows the ability to bridge the gap between data and business.
This aspect is best evaluated during interviews, where candidates have the opportunity to explain their thought process and reasoning. Candidates who can translate data-driven insights into actionable recommendations display excellent communication.
b. Cultural Fit
Cultural fit goes beyond mere communication. It’s about whether the candidate aligns with the values and ways of working within the organization. For example, in a fast-paced startup environment, adaptability and a proactive attitude are essential. On the other hand, larger organizations may require more teamwork, structure, and alignment with processes.
Cultural fit can often be assessed during behavioral interviews, where situational or experience-based questions reveal how the candidate approaches challenges and relationships.
c. Attitude and Positivity
The attitude a candidate brings can make a significant difference in their ability to contribute to a team. A positive attitude, resilience in the face of obstacles, and a willingness to learn can distinguish a good data scientist from a great one. Look for candidates who exhibit enthusiasm and curiosity about new challenges, as this often correlates with innovation and a proactive work ethic.
d. Emotional Intelligence
Emotional intelligence (EQ) relates to a candidate’s ability to manage their emotions and relationships effectively. A candidate with high EQ will likely be able to navigate team dynamics, handle feedback constructively, and work well under pressure. EQ is a strong indicator of whether a candidate can be trusted to handle difficult situations with grace and professionalism.
e. Ethics and Integrity
Given the power that data science holds, ethical considerations should not be overlooked. Whether it’s responsible data usage or algorithmic transparency, data scientists must display integrity in their work. This can be evaluated during the interview by discussing ethical dilemmas they may have faced and how they handled them.
Stage of Evaluation: Assessment Stage (Phone/Onsite Interviews)
Behavioral aspects are best evaluated when there is real-time interaction with the candidate. This typically occurs in the assessment stage, where interviews, both phone and onsite, provide the opportunity to ask behavioral questions and gauge communication and collaboration skills.
3. Do They Have Potential for Growth? (Potential)
Hiring a data scientist isn’t just about the present; it’s about their future potential to evolve within the organization. The field of data science is rapidly changing, and candidates who can learn, adapt, and take on new challenges are the ones who will add long-term value. This vertical asks: Does this person have the potential to grow?
Key Areas to Evaluate:
a. Curiosity and Motivation
A candidate with genuine curiosity and passion for data science will likely stay on top of trends, learn new skills, and push the boundaries of innovation. Curiosity often shows up in the questions they ask during interviews, the side projects they undertake, and their engagement in the broader data science community.
b. Vision and Goals
Potential can be gauged by asking about a candidate’s long-term goals. Do they have a clear vision for their career, and does that align with the organization’s direction? Candidates with ambition and goals often push themselves to take on new challenges and roles.
c. Fast Learning and Agility
The ability to quickly learn new tools, frameworks, and methods is critical in data science. A candidate’s agility in learning can be evaluated by reviewing how they’ve evolved over time in previous roles. Have they consistently taken on new challenges, or are they stagnant in their skills? Agility and willingness to learn indicate a candidate's potential to adapt to future changes.
d. Resilience
Resilience is a key factor in determining a candidate’s potential to grow. Can they bounce back from failure? In data science, not all experiments work, and the candidate must be able to handle setbacks, iterate, and move forward without becoming discouraged.
Stage of Evaluation: From Review Stage Onward
Potential is something that can be evaluated from the candidate review stage onward. You can get hints from their resume about how their career has progressed and how they’ve sought out new challenges. Further insight can be gained during interviews by discussing their goals, curiosity, and how they handle learning and growth opportunities.
Conclusion
Hiring the right data scientist involves evaluating candidates across three key verticals: skills, behavioral aspects, and potential for growth. Each of these dimensions is important, and failing to consider any of them can lead to a poor hire. By focusing on technical competence, ensuring cultural fit, and identifying growth potential, organizations can hire data scientists who not only excel in their current roles but who will also continue to drive success in the future.
Hiring is an investment. Evaluating these three verticals thoroughly can help ensure that you’re bringing in the right talent to meet both present and future needs.