🎯 ML, DL, GenAI - What Do You Really Need?

It’s not about who knows the most models. It’s about who can solve the problem with the right approach.

🚀 In interviews and real-world projects, here’s what separates noise from value:

• Can they choose the right approach? → Classical ML, Deep Learning, or GenAI not everything needs the latest hype.
• Do they know when not to use GenAI? → It’s impressive to know LLMs. It’s smarter to know when not to call them.
• Can they debug when pre-built solutions fail? → You don’t need a model zoo, you need people who can trace the issue and fix it.
• Can they explain their trade-offs and iterate with clarity? → Choosing between latency, accuracy, explainability, and cost is real work.

💡 Skip the overly academic or overly abstract interviews. Hire those who think in problem-first, data-smart, solution-aware ways.

✅ Evaluate with real-world scenarios.
✅ Prioritize learning agility and debugging mindset.
✅ Look for clarity in reasoning, not just complexity in vocabulary.

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#MLvsDLvsGenAI #AIHiring #GenAIRealityCheck #DataDrivenEngineering #AIProductThinking #ProblemFirst #ResponsibleAI #TechRecruiting #DebuggingMatters #RealWorldAI #InterviewWisdom #EnterpriseAI #ThinkBuildLearn

 Keep Thinking!!!

 

The Evolving Landscape of ML Hiring: A Veteran's Perspective

 

Job interviews often miss true talent. They reward rehearsed responses over candidates who can persistently build practical, context-aware solutions beyond just technical know-how

As someone in the trenches of data science hiring for over 7 years, I've watched our field transform dramatically. Recently, a job description for an ML role caught my eye - and not necessarily in a good way. It got me thinking about how our industry's hiring practices often need to catch up to the reality of our work. Let me share some observations:

The Commodity of Code

• LLM can generate working solutions / provide ideas / get started on any topic as long as you have good basic skills and coding knowledge. Now, I ask interns hiring assignment tasks to focus on accuracy and bugs. Code has become a commodity. The real value lies in understanding models, and limitations, bridging the gap between visions and technical realities, and architecting solutions that solve real-world problems.

The Kitchen Sink JD

• This particular job description reads like a wish list for a tech superhero. Data structures, algorithms, AI/ML, coding, system design - oh, and don't forget a dash of product sense! While it's great to aim high, this scattergun approach often misses the mark. We need specialists with deep expertise, not generalists who've dabbled in everything.

The Interview Gauntlet

• The hiring process outlined was a marathon: write-ups, HackerEarth assessments, coding tests, multiple rounds with the ML team, and then more conversations. In a market where top talent is scarce and in high demand, do we really need to put candidates through such a lengthy ordeal?

The Missing Pieces

• What struck me most was what the JD and process didn't emphasize. Where was the assessment of a candidate's ability to translate business problems into technical solutions? How about evaluating their capacity to stay ahead of rapidly evolving trends in ML?

A Call for Pragmatism

• To my fellow hiring managers and HR teams: let's get practical. The perfect candidate who ticks every box on your mile-long list probably doesn't exist - and if they do, they're likely happily employed or running their own startup.

Instead, focus on core competencies that drive real value:

• The ability to understand and translate business needs
• A knack for architecting scalable, efficient solutions
• Adaptability and a passion for continuous learning
• Strong communication skills to bridge technical and non-technical stakeholders

The ML landscape is changing faster than ever. Our hiring practices need to keep pace. Let's move beyond the "code on a whiteboard" era and design processes that identify true innovators who can propel our field forward.

Another Good Read - Why We Don't Interview Product Managers Anymore

Got Something worthy Today to post

This JD Rocks - Link

• Focus on practical software engineering, not algorithm challenges.
• Work through a system design problem relevant to your daily work.
• Talk about your perspectives on building a great product.
• Deep dive on engineering practices and culture 

Keep Exploring!!!

Balancing code - data - business

To generate value, one needs to solve real problems, to solve problems needs to know business execution at ground level, this just translates to people, data, information, and touchpoints. Taking some info from ground level, Adding the pieces with your ideas, and Building a working idea is the balance of feasibility vs solution analysis. We may not know everything 100% but learning things on the go/need basis and learning to make progress in the interest of time is key. The feeling of once you do two or three examples, This is all it is all about that becomes paramount :)

The ability to learn / self-manage / progress with directions / see the big picture is more important than remembering code syntax. I believe it's a balance of all, not just algos. How you know where to apply, and when to apply is much beyond kaggle and competitive tests :)

• Learning business
• Knowing Touchpoints 
• Data Exchange patterns - Methods of information exchange (Batched / Real-time)
• Tech tools landscape
• Prototype code up
• 30% business, 30% data, 30% code to get perspective

My Hiring Recommendations

• Hire someone who knows what business problem they solve
• How they design based on time constraints
• Tradeoff on Tech vs Time vs Design
• Thinking for scale/performance
• Need not be full stack but knows how to communicate and get things done
• Customer is more important than individuals EGO's

Keep Exploring!!

Hiring perspectives

I have two candidates

• Candidate #1 - Cleared Data Science Test, Good Deep Learning, Has not worked in Pharma domain but good at ML
• Candidate #2 - Just passed ML Test, No Deep Learning Expertise but worked in Pharma, Has domain and insights, Data knowledge

Both performed well in other behavioral rounds. If we end up hiring multiple types of Candidate 1 effectively you will end up with #duplicate skill set. I would prefer to have a mix of both in a team. Skills need to complement and add different perspectives to the problem. In a team, we need a combination of both #1 and #2. We need to have different #proportions of #skills and assessments to get a good mix of talent that can look at #same #problem in multiple #perspectives

Keep Thinking!!

Data Science Skills / Challenges

• Ideate - Domain Knowledge, Contextual AI / Data Knowledge
• Design - Algorithms, Data, Features
• Develop - Cloud, Data, ML
• Implement AI - Deploy, End to End Architecture

Observations / Challenges

• Desire to satisfy their intellectual curiosity rather than because a project or technique stands
• Business contexts are those who think about how to scale their algorithms in production
• Passionate about building faster, more efficient data pipelines
• Experienced DS managers who serve as product managers to guide/develop / envision
• The greater the ambiguity of a situation or project, the more important it is to hire people with a commercial and strategic understanding
• Hire talent that has both specialized expertise in AI and business acumen

Keep Exploring!!!

Hiring thoughts

• Hire for generalists - Domain Expertise / Products, Fintech, Agriculture, Automotive, Digital Twins - Telematics
• Hire for specialists - MLOps, Kubernetes, Stats
• Hire for interests - Outside work, domains/product observations, How you see tech and business future
• Code for Solutions and Products - Build a comprehensive solution as well include both aspirations/team mix / demonstrated past contributions outside work
• Map the charter - Inhouse products / SaaS products / Competency building

Keep Exploring!!!

Data Science Hiring Thoughts

New age Resume

• Demonstrate business knowledge
• Demonstrate working prototypes
• Demonstrate technology adoption and value realization

My perspectives run in a different direction. I learn the required technology pieces to get my idea of working. I am not really focused on mastering technology but leveraging it and learning to solve my implementation.

For me over years focus shifted from technology to business, business to futuristic needs. You can only build a future when you can envision the future. The future is built on optimism, creativity, technology, and making it more accessible and affordable.

Build your portfolio and perspective not just your technical skills. - Siva 

More Reads

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Key Lessons

• Numbers are much more powerful than the same bullet point without numbers.
• Increased user engagement 27% by refactoring our front-end experience in React.

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Being able to feel empathy and to take in the other person's perspective

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How to write a resume that converts

Happy Learning!!!