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#datascience #generativeai #agenticai
#datascience #generativeai #agenticai
𝗠𝘆 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 (≈ 6 𝗥𝗼𝘂𝗻𝗱𝘀)
Here are the stages I went through, plus what I gathered from others online:
⸻
𝟭. 𝗥𝗲𝘀𝘂𝗺𝗲 𝗦𝗵𝗼𝗿𝘁𝗹𝗶𝘀𝘁𝗶𝗻𝗴 / 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿 𝗦𝗰𝗿𝗲𝗲𝗻
The recruiter reviews your resume to check alignment with technical skills (SQL, data pipelines, cloud tools like Azure, Spark etc.) and project experience.
They may also ask about your background, motivation, and career goals. Strong communication and a clear resume really help.
𝟮. 𝗧𝘄𝗼 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗥𝗼𝘂𝗻𝗱𝘀 (1 Hour Each)
These rounds dive deep into technical expertise. Common topics:
• SQL performance & optimization
• Data modelling
• Pipeline & ETL design
• Handling edge cases
• Cloud services (Azure Data Factory, Databricks, Synapse)
• DSA questions on Arrays & Linked Lists, Queue
One round may involve system/architecture design (e.g., scalable data warehouse, streaming pipeline). Another may focus on coding or troubleshooting data pipelines.
⸻
𝟯. 𝗛𝗶𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗥𝗼𝘂𝗻𝗱
This round mixes technical and behavioural aspects. The manager checks for:
• Problem-solving ability
• Ownership
• Stakeholder management
𝟰. 𝗔𝗔 (𝗔𝘀 𝗔𝗽𝗽𝗿𝗼𝗽𝗿𝗶𝗮𝘁𝗲) / 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝗶𝗮𝗹 𝗥𝗼𝘂𝗻𝗱
A senior-level evaluation focusing on leadership, collaboration, and cultural fit.
You may face behavioural questions about handling ambiguity, conflict, mentoring, and driving impact across teams.
They may also ask how you ensure scalability, quality, and reliability in data systems.
𝟱. 𝗛𝗥 𝗥𝗼𝘂𝗻𝗱: 𝗦𝗮𝗹𝗮𝗿𝘆 & 𝗢𝗳𝗳𝗲𝗿 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻
Covers compensation (base, bonus, stocks), benefits, role level, and formalities like relocation or background checks.
Here are the stages I went through, plus what I gathered from others online:
⸻
𝟭. 𝗥𝗲𝘀𝘂𝗺𝗲 𝗦𝗵𝗼𝗿𝘁𝗹𝗶𝘀𝘁𝗶𝗻𝗴 / 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿 𝗦𝗰𝗿𝗲𝗲𝗻
The recruiter reviews your resume to check alignment with technical skills (SQL, data pipelines, cloud tools like Azure, Spark etc.) and project experience.
They may also ask about your background, motivation, and career goals. Strong communication and a clear resume really help.
𝟮. 𝗧𝘄𝗼 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗥𝗼𝘂𝗻𝗱𝘀 (1 Hour Each)
These rounds dive deep into technical expertise. Common topics:
• SQL performance & optimization
• Data modelling
• Pipeline & ETL design
• Handling edge cases
• Cloud services (Azure Data Factory, Databricks, Synapse)
• DSA questions on Arrays & Linked Lists, Queue
One round may involve system/architecture design (e.g., scalable data warehouse, streaming pipeline). Another may focus on coding or troubleshooting data pipelines.
⸻
𝟯. 𝗛𝗶𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗥𝗼𝘂𝗻𝗱
This round mixes technical and behavioural aspects. The manager checks for:
• Problem-solving ability
• Ownership
• Stakeholder management
𝟰. 𝗔𝗔 (𝗔𝘀 𝗔𝗽𝗽𝗿𝗼𝗽𝗿𝗶𝗮𝘁𝗲) / 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝗶𝗮𝗹 𝗥𝗼𝘂𝗻𝗱
A senior-level evaluation focusing on leadership, collaboration, and cultural fit.
You may face behavioural questions about handling ambiguity, conflict, mentoring, and driving impact across teams.
They may also ask how you ensure scalability, quality, and reliability in data systems.
𝟱. 𝗛𝗥 𝗥𝗼𝘂𝗻𝗱: 𝗦𝗮𝗹𝗮𝗿𝘆 & 𝗢𝗳𝗳𝗲𝗿 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻
Covers compensation (base, bonus, stocks), benefits, role level, and formalities like relocation or background checks.
Dm us on whatsapp +9183182 38637 for training enquiry. It is 4 months training program
Batch starting from November
Batch starting from November
AI For Data Engineers (1).pdf
253.8 KB
AI For Data Engineers (1).pdf
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What is weird generalization In Machine learning
Weird generalization usually refers to a surprising behavior of machine learning models where they perform well on data they were never explicitly trained for, but in a way that doesn’t align with human intuition.
In simple terms
A model learns patterns that work, but not necessarily the patterns humans expect.
Weird generalization usually refers to a surprising behavior of machine learning models where they perform well on data they were never explicitly trained for, but in a way that doesn’t align with human intuition.
In simple terms
A model learns patterns that work, but not necessarily the patterns humans expect.
Problems with Today's large language model
Follow us on Instagram www.instagram.com/dataspoof for data science and genai updates
Follow us on Instagram www.instagram.com/dataspoof for data science and genai updates
Today most of the student or working professionals get failed on their system design interviews for their roles on machine learning engineer, data scientist and AI Engineer.
So we here at DataSpoof, Our team prepared Notes on the system design (more than 100+ case studies) from more than 70+ top companies
https://rzp.io/rzp/8Wl4MBZY
So we here at DataSpoof, Our team prepared Notes on the system design (more than 100+ case studies) from more than 70+ top companies
https://rzp.io/rzp/8Wl4MBZY
Razorpay
Pay for System Design Notes for Machine learning by DataSpoof
Today most of the student or working professionals get failed on their system design interviews for their roles on machine learning engineer, data scientist and AI Engineer.. . So we here at DataSpoof, Our team prepared Notes on the system design (more than…
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