The Manager Matters Most: A Guide to Spotting Bad Bosses in Interviews

2/6/2025

I. Introduction
This recent survey of 8000+ tech professionals (May 2025) by Lenny Rachitsky and Noam Segal caught my eye. For anyone interested in a career in tech or already working in this sector, it is a highly recommended read. The blog is full of granular insights about various aspects of work - burnout, career optimism, working in startups vs. big tech companies, in-office vs. hybrid vs. remote work, impact of AI etc.

However, the insight that really caught my eye is the one shared above highlighting the impact of direct-manager effectiveness on employees' sentiment at work. It's a common adage that 'people don't leave companies, they leave bad managers', and the picture captured by Lenny's survey really hits the message home.

The delta in work sentiment on various dimensions (from enjoyment to engagement to burnout) between 'great' and 'ineffective' managers is so obviously large that you don't need statistical error bars to highlight the effect size!

The quality of leadership has never been more important given the double whammy of massive layoffs of tech roles and the impact of generative AI tools in contributing to improved organisational efficiencies that further lead to reduced headcount.

In my recent career coaching sessions with mentees seeking new jobs or those impacted by layoffs, identifying and avoiding toxic companies, work cultures and direct managers is often a critical and burning question.

Although one may glean some useful insights from online forums like Blind, Reddit, Glassdoor, these platforms are often not completely reliable and have poor signal-to-noise in terms of actionable advice. In this blog, I dive deeper into this topic and highlight common traits of ineffective leadership and how to identify these traits and spot red flags during the job interview process.

II. Common Characteristics of Ineffective Managers

These traits are frequently cited by employees:

Poor Communication: This is a cornerstone of bad management. It manifests as unclear expectations, lack of feedback (or only negative feedback), not sharing relevant information, and poor listening skills. Employees often feel lost, unable to meet undefined goals, and undervalued.
Micromanagement: Managers who excessively control every detail of their team's work erode trust and stifle autonomy. This behavior often stems from a lack of trust in employees' abilities or a need for personal control. It kills creativity and morale.
Lack of Empathy and Emotional Intelligence: Toxic managers often show a disregard for their employees' well-being, workload, or personal circumstances. They may lack self-awareness, struggle to understand others' perspectives, and create a stressful, unsupportive environment.
Taking Credit and Blaming Others: A notorious trait where managers appropriate their team's successes as their own while quickly deflecting blame for failures onto their subordinates. This breeds resentment and distrust.
Favoritism and Bias: Unequal treatment, where certain employees are consistently favored regardless of merit, demotivates the rest of the team and undermines fairness.
Avoiding Conflict and Responsibility: Inefficient managers often shy away from addressing team conflicts or taking accountability for their own mistakes or their team's shortcomings. This can lead to a festering negative environment.
Lack of Support for Growth and Development: Good managers invest in their team's growth. Incompetent or toxic ones may show no interest in employee development, or worse, actively hinder it to keep high-performing individuals in their current roles.
Unrealistic Expectations and Poor Planning: Setting unachievable goals without providing adequate resources or clear direction is a common complaint. This often leads to burnout and a sense of constant failure.
Disrespectful Behavior: This can include public shaming, gossiping about employees or colleagues, being dismissive of ideas, interrupting, and generally creating a hostile atmosphere.
Focus on Power, Not Leadership: Managers who are more concerned with their authority and being "the boss" rather than guiding and supporting their team often create toxic dynamics. They may demand respect rather than earning it.
Poor Work-Life Balance Encouragement: Managers who consistently expect overtime, discourage taking leave, or contact employees outside of work hours contribute to a toxic culture that devalues personal time.
High Turnover on Their Team: While not a direct trait of the manager, a consistent pattern of employees leaving a specific manager or team is a strong indicator of underlying issues.

III. Identifying These Traits and Spotting Red Flags During the Interviews:
The interview process is a two-way street. It's your opportunity to assess the manager and the company culture. Here's how to look for red flags, based on advice shared in online communities:

A. During the Application and Initial Research Phase:

Vague or Unrealistic Job Descriptions: As highlighted on sites like Zety and FlexJobs, job descriptions that are unclear about responsibilities, list an excessive number of required skills for the pay grade, or use overly casual/hyped language ("rockstar," "ninja," "work hard, play hard," "we're a family") can be warning signs. "We're a family" can sometimes translate to poor boundaries and expectations of excessive loyalty.
Negative Company Reviews: Pay close attention to reviews mentioning specific management issues, high turnover, lack of work-life balance, and a toxic culture. Look for patterns in the complaints.
High Turnover in the Role or Team: LinkedIn research can be insightful. If the role you're applying for has been open multiple times recently, or if team members under the hiring manager have short tenures, it's a significant red flag.

B. During the Interview(s):

How the Interviewer Behaves:

Disorganized or Unprepared: Constantly rescheduling, being late, not knowing your resume, or seeming distracted are bad signs. This can reflect broader disorganization within the company or a lack of respect for your time.
Dominates the Conversation/Doesn't Listen: A manager who talks excessively about themselves or the company without giving you ample time to speak or ask questions may not be a good listener or value employee input.
Vague or Evasive Answers: If the hiring manager is unclear about the role's expectations, key performance indicators, team structure, or their management style, it's a concern. Pay attention if they dodge questions about team challenges or career progression.
Badmouthing Others: If the interviewer speaks negatively about current or former employees, or even other companies, it demonstrates a lack of professionalism and respect.
Focus on Negatives or Pressure Tactics: An interviewer who heavily emphasizes pressure, long hours, or seems to be looking for reasons to disqualify you can indicate a stressful or unsupportive environment. Phrases like "we expect 120%" or "we need someone who can hit the ground running with no hand-holding" can be red flags if not balanced with support and resources.
Lack of Enthusiasm or Passion: An interviewer who seems disengaged or uninterested in the role or your potential contribution might reflect a demotivated wider team or poor leadership (Mondo).
Inappropriate or Illegal Questions: Questions about your age, marital status, family plans, religion, etc., are not only illegal in many places but also highly unprofessional.
Dismissive of Your Questions or Concerns: A good manager will welcome thoughtful questions. If they seem annoyed or brush them off, it's a bad sign.

Questions to Ask the Hiring Manager and what to watch out for:

"How would you describe your leadership style?" (Listen for buzzwords vs. concrete examples).
"How does the team typically handle [specific challenge relevant to the role]?"
"How do you provide feedback to your team members?" (Look for regularity and constructiveness).
"What are the biggest challenges the team is currently facing, and how are you addressing them?"
"How do you support the professional development and career growth of your team members?" (Vague answers are a red flag).
"What does success look like in this role in the first 6-12 months?" (Are expectations clear and realistic?).
"Can you describe the team culture?" (Compare their answer with what you observe and read in reviews).
"What is the average tenure of team members?" (If they are evasive, it's a concern).
"How does the company handle work-life balance for the team?"

Questions to Ask Potential Team Members:

"What's it really like working for [Hiring Manager's Name]?"
"How does the team collaborate and support each other?"
"What opportunities are there for learning and growth on this team?"
"What is one thing you wish you knew before joining this team/company?"
"How is feedback handled within the team and with the manager?"

Red Flags in the Overall Process:

Excessively Long or Disjointed Hiring Process: While thoroughness is good, a chaotic, overly lengthy, or unclear process can indicate internal disarray.
Pressure to Accept an Offer Quickly: A reasonable employer will give you time to consider an offer. High-pressure tactics are a red flag.
The "Bait and Switch": If the role described in the offer differs significantly from what was discussed or advertised, this is a major warning.
No Opportunity to Meet the Team: If they seem hesitant for you to speak with potential colleagues, it might be because they are trying to hide existing team dissatisfaction.

IV. Conclusion
The importance of intuition and trusting your gut cannot be overemphasised enough. If something feels "off" during the interview process, even if you can't pinpoint the exact reason, pay attention to that feeling. The interview is often a curated glimpse into the company; if red flags are apparent even then, the day-to-day reality at work could be much worse.

By combining common insights from fellow peers and mentors with careful observation and targeted questions during the interview process, you can significantly improve your chances of identifying and avoiding incompetent, inefficient, or toxic managers and finding a healthier, more supportive work environment.

Comments

How do I crack a Data Science Interview, and do I also have to learn DSA?

18/5/2025

Comments

Cracking data science and, increasingly, AI interviews at top-tier companies has become a multifaceted challenge. Whether you're targeting a dynamic startup or a Big Tech giant, and regardless of the specific level, you should be prepared for a rigorous interview process that can involve 3 to 6 or even more rounds. While the core areas remain foundational, the emphasis and specific expectations have evolved.

The essential pillars of data science and AI interviews typically include:

Statistics and Probability: Expect in-depth questions on statistical inference, hypothesis testing, experimental design, probability distributions, and handling uncertainty. Interviewers are looking for a strong theoretical understanding and the ability to apply these concepts to real-world problems.
Programming (Primarily Python): Proficiency in Python and relevant libraries (like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch) is non-negotiable. Be prepared for coding challenges that involve data manipulation, analysis, and even implementing basic machine learning algorithms from scratch. Familiarity with cloud computing platforms (AWS, Azure, GCP) and data warehousing solutions (Snowflake, BigQuery) is also increasingly valued.
Machine Learning (ML) & Deep Learning (DL): This remains a core focus. Expect questions on various algorithms (regression, classification, clustering, tree-based methods, neural networks, transformers), their underlying principles, assumptions, and trade-offs. You should be able to discuss model evaluation metrics, hyperparameter tuning, bias-variance trade-off, and strategies for handling imbalanced datasets. For AI-specific roles, a deeper understanding of deep learning architectures (CNNs, RNNs, Transformers) and their applications (NLP, computer vision, etc.) is crucial.
AI System Design: This is a rapidly growing area of emphasis, especially for roles at Big Tech companies. You'll be asked to design end-to-end AI/ML systems for specific use cases, considering factors like data ingestion, feature engineering, model selection, training pipelines, deployment strategies, scalability, monitoring, and ethical considerations.
Product Sense & Business Acumen: Interviewers want to assess your ability to translate business problems into data science/AI solutions. Be prepared to discuss how you would approach a business challenge using data, define relevant metrics, and communicate your findings to non-technical stakeholders. Understanding the product lifecycle and how AI can drive business value is key.
Behavioral & Leadership Interviews: These rounds evaluate your soft skills, teamwork abilities, communication style, conflict resolution skills, and leadership potential (even if you're not applying for a management role). Be ready to share specific examples from your past experiences using the STAR method (Situation, Task, Action, Result).
Problem-Solving, Critical Thinking, & Communication: These skills are evaluated throughout all interview rounds. Interviewers will probe your thought process, how you approach unfamiliar problems, and how clearly and concisely you can articulate your ideas and solutions.

The DSA Question in 2025: Still Relevant?The relevance of Data Structures and Algorithms (DSA) in data science and AI interviews remains a nuanced topic. While it's still less critical for core data science roles focused primarily on statistical analysis, modeling, and business insights, its importance is significantly increasing for machine learning engineering, applied scientist, and AI research positions, particularly at larger tech companies.
Here's a more detailed breakdown:

Core Data Science Roles: If the role primarily involves statistical analysis, building predictive models using off-the-shelf libraries, and deriving business insights, deep DSA knowledge might not be the primary focus. However, a basic understanding of data structures (like lists, dictionaries, sets) and algorithmic efficiency can still be beneficial for writing clean and performant code.
Machine Learning Engineer & Applied Scientist Roles: These roles often involve building and deploying scalable ML/AI systems. This requires a stronger software engineering foundation, making DSA much more relevant. Expect questions on time and space complexity, sorting and searching algorithms, graph algorithms, and designing efficient data pipelines.
AI Research Roles: Depending on the research area, a solid understanding of DSA might be necessary, especially if you're working on optimizing algorithms or developing novel architectures.

In 2025, the lines are blurring. As AI models become more complex and deployment at scale becomes critical, even traditional "data science" roles are increasingly requiring a stronger engineering mindset. Therefore, it's generally advisable to have a foundational understanding of DSA, even if you're not targeting explicitly engineering-focused roles.
Navigating the Evolving Interview LandscapeGiven the increasing complexity and variability of data science and AI interviews, the advice to learn from experienced mentors is more critical than ever. Here's why:

Up-to-date Insights: Mentors who are currently working in your target roles and companies can provide the most current information on interview formats, the types of questions being asked, and the skills that are most valued.
Tailored Preparation: They can help you identify your strengths and weaknesses and create a personalized preparation plan that aligns with your specific goals and the requirements of your target companies.
Realistic Mock Interviews: Experienced mentors can conduct realistic mock interviews that simulate the actual interview experience, providing valuable feedback on your technical skills, problem-solving approach, and communication.
Insider Knowledge: They can offer insights into company culture, team dynamics, and what it takes to succeed in those environments.
Networking Opportunities: Mentors can sometimes connect you with relevant professionals and opportunities within their network

In conclusion, cracking data science and AI interviews in 2025 requires a strong foundation in core technical areas, an understanding of AI system design principles, solid product and business acumen, excellent communication skills, and increasingly, a grasp of fundamental data structures and algorithms. Learning from experienced mentors who have navigated these challenging interviews successfully is an invaluable asset in your preparation journey.

The Manager Matters Most: A Guide to Spotting Bad Bosses in Interviews

How do I crack a Data Science Interview, and do I also have to learn DSA?

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