[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-160131-en":3,"doc-seo-160131-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},160131,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",4,"Exam","GradientAscent - Interview Experiences","GradientAscent documents real interview experiences from an IIT Guwahati consulting & analytics club, covering internships and placements with a focus on machine learning and data science. It records coding and data-analysis tasks, then follows up with technical questioning across ML/DL concepts such as activation functions, loss functions, backpropagation, and RNN training issues. It also captures time-series assignment rounds, PCA and linear regression fundamentals, eligibility criteria, and assessment formats, highlighting strengths, missteps, and outcomes including whether candidates advanced to HR.","Consulting &Analytics clubIIT Guwahati  \n2024-25  \n# GradientAscent\n\nINTERVIEW EXPERIENCES  \nA Detailed guide for Internshipsand Placements focusing onMachine Learning &Data Science  \n# Varun Tej\n\nData Scientist  \nIntern |2024  \nThe interview began with a coding challenge where I was given adata frame with three columns:emp_id,dept,and salary.The taskwas to identify all employees who had the second-highest salarywithin their department,and if two employees shared the samesecond-highest salary,to return the one whose first name appearedin ascending order.This could be implemented in Python or SQL.Unfortunately,as I hadn't revised SQL thoroughly,I was unable tosolve the problem effectively.  \nFollowing this,the interviewer asked if I was more comfortable withdeep learning or machine learning,to which I responded that I hadprimarily worked with deep learning.The questions that followedcovered a range of deep learning and machine learning topics,including:  \n1.Why parameters are necessary for ML/DL models.  \n2.Activation functions,their purpose,explanations of the variousactivation functions I knew,and their derivatives.  \n3.How ReLU introduces non-linearity.  \n4.The loss functions I've used,along with their explanations andderivatives.  \n5.Evaluation metrics for regression problems,their advantages,anddisadvantages.  \n6.An explanation of backpropagation and the equations involved.  \n7.How backpropagation is implemented in RNNs.  \n8.The vanishing gradient problem in RNNs,how to mitigate it,andexamples where RNNs might avoid this issue.  \nThe interview was challenging,and I struggled to answer 2-3 of thesetechnical questions.Given my difficulties with both the coding taskand some of the deep learning questions,I did not progress to the HRround.  \n# Shamith K\n\nData Scientist  \nIntern |2024  \nI recently participated in an interview round that followed a machinelearning(ML)assignment based on time series analysis using thecompany's data.Out of the candidates,10 were shortlisted for thisround,which lasted for 45 minutes.  \nIn the initial 5 minutes,I introduced myself,emphasizing myexperience with finance-related projects from my first and secondyears and mentioning that I was still in the early stages of my MLjourney.This led the interviewer to ask more about the probability andstatistics aspects,along with some questions related to the assignmentprovided.  \nI then explained one of my finance projects,which involved deployingan ML model.Up until this point,the interview was going smoothly.However,as the discussion delved deeper into the technical intricaciesof ML,my performance had some hits and misses.  \nUnfortunately,I was not able to clear this round.Out of the 10candidates,only two progressed further,with one eventually beingselected.  \nAkanksh Khandelwal  \n# Data Scientist\n\nIntern |2024  \nKLA  \nThe eligibility criteria for this interview was a CPl of 7.5 or higher,and only students from CSE,MnC,DSAI,EEE,ECE,and EP wereallowed to participate.The online assessment consisted of multiple-choice questions focused on machine learning(ML)and datascience,along with one easy coding problem.Six candidates wereshortlisted for the interview round.  \nDuring the interview,I was first asked to introduce myself,followedby questions on principal component analysis (PCA),matrices,linearregression,and the time complexity of matrix inversion.The  \nquestions were largely centered on data science and algorithms.As Iwas not specifically targeting ML services,I repeatedly requested theinterviewer to ask questions related to High-Performance Computing(HPC)or Competitive Programming (CP).Toward the end,he askeda simple dynamic programming(DP)question,which I was able tosolve in two minutes.  \nAlthough the interviewer was pleased with my performance,I believethey were seeking someone with deeper expertise in Al and ML.Ultimately,no candidates were selected from the interview process.  \n# Udit Jethva\n\nData Scientist  \nIntern |2024  \n## Round 1:Online Assessm","cbCaik7qJVSjLaiV","https://ap.wps.com/l/cbCaik7qJVSjLaiV","pdf",5626271,1,42,"English","en",105,"# Interview Experiences\n## Varun Tej\n## Shamith K\n## Akanksh Khandelwal\n## Udit Jethva","[{\"question\":\"What kinds of technical questions are asked in these ML/data science interviews?\",\"answer\":\"Questions commonly cover both fundamentals and implementation ideas, including activation and loss functions, backpropagation equations, evaluation metrics, and RNN-related problems like vanishing gradients.\"},{\"question\":\"Do the interviews include coding challenges along with theory questions?\",\"answer\":\"Yes. The documented interviews start with coding or assessment tasks such as SQL/Python data-frame transformations or easier coding problems, followed by deeper technical discussions.\"},{\"question\":\"What topics appear in the time-series based interview round?\",\"answer\":\"The round includes questions tied to probability and statistics, along with discussion of the provided machine learning assignment using the company’s time-series data.\"}]","GradientAscent - Interview Experiences | PDF",1788050737,106,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"gradientascent-interview-experiences","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/exam/",3,{"item":52,"name":13,"@type":43,"position":11},"https://docshare.wps.com/document/gradientascent-interview-experiences/160131/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-30",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What kinds of technical questions are asked in these ML/data science interviews?","Question",{"text":74,"@type":75},"Questions commonly cover both fundamentals and implementation ideas, including activation and loss functions, backpropagation equations, evaluation metrics, and RNN-related problems like vanishing gradients.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Do the interviews include coding challenges along with theory questions?",{"text":79,"@type":75},"Yes. The documented interviews start with coding or assessment tasks such as SQL/Python data-frame transformations or easier coding problems, followed by deeper technical discussions.",{"name":81,"@type":72,"acceptedAnswer":82},"What topics appear in the time-series based interview round?",{"text":83,"@type":75},"The round includes questions tied to probability and statistics, along with discussion of the provided machine learning assignment using the company’s time-series data.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,103,108,113,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":101,"slug":102},70,"exam",{"id":104,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":46,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":104,"slug":138},19,"General","general"]