HiringBranch’s Post

For all the tech being built on public LLMs, these large models are not always the best solution to train AI algorithms. Case in point: At HiringBranch, we tested 3 open source LLMs against our smaller dataset and HiringBranch's proved to be 97% accurate versus Google's FLAN-T5-Large language model at 64% accuracy. With the other models showing even less accurate results. What a difference! Reason being, the LLMs don't have enough of the *right* data to solve the specific problem of what we do: measure soft skills in the hiring process. Voilà! Happy Friday 🙌🏻

🔍💼 What is the ongoing trend in the interview process for a Data Scientist? 📈🔬 The process may differ slightly across companies, but the overall gist remains the same: ML fundamentals, Data-structure & Algorithms, and ML system designing skills are tested. 🧠💻 Here's a short video for Data Science enthusiasts I have created on the topic of the ongoing data science interview process trends. 📹📚 Video Link: https://lnkd.in/e7EKhZiJ YT channel Link:  youtube.com/@datatrek 👍🔔 Like and subscribe for more such interesting concepts. Also, like and share over here for maximum reach. 😉✨ Let's stay updated with the latest interview trends and ace those Data Science interviews! 💪🚀 #datatrek #datascience #machinelearning #statistics #deeplearning #ai

Post-Interview Insights: What Matters in a Data Science Interview Had an interesting interview today, and it reinforced a recurring theme I've noticed across many interviews: employers are keenly interested in how you think about the data science process—not just the models you know. Here’s what they focus on: Validation Techniques: It’s not enough to list out k-fold or leave-one-out cross-validation; they want to hear about how each approach helps identify overfitting and why you’d choose one over the other in different scenarios. Sampling and Evaluation: Knowing your sampling techniques and evaluation metrics demonstrates your understanding of working with imbalanced datasets, noisy data, and measuring true model performance. Data Preprocessing & Feature Engineering: Before even thinking about model selection, they want to see that you prioritize transforming, cleaning, and enriching the data to ensure the model has quality inputs. Hyperparameter Tuning: Simply running grid search is common, but knowing why you chose certain ranges or methods (e.g., Bayesian optimization) shows a deeper understanding of model refinement. It's clear they’re not looking for someone who can just call .fit() on a library function—they want someone who can truly evaluate and enhance a model’s performance with careful, strategic decisions throughout the pipeline. A big takeaway for aspiring data scientists: it’s less about memorizing complex models and more about developing a strong foundation in validation, evaluation, data handling, and model tuning. These skills are what set apart great data scientists, especially in today’s competitive job market. Do you agree? #DataScience #MachineLearning #ModelEvaluation #DataPreprocessing #FeatureEngineering #HyperparameterTuning #Overfitting #InterviewTips #DataScienceInterview #CareerGrowth #AI #MachineLearningTips #DataDriven #MLPractices #CareerAdvice

How I Answered Behavioral Questions in Data Science and ML Interviews ✨ When I first started preparing for data science interviews, the technical rounds felt easy. I could code, build models, explain algorithms, and business impact. But the behavioural questions? They stumped me. "Tell me about a time you solved a challenging data problem." "How do you handle disagreements with stakeholders or peers?" "Describe a project where you collaborated with a cross-functional team." I learned the hard way that technical skills get your foot in the door, but your stories leave a lasting impression. Here’s the framework that changed the game for me: 🔹 S.T.A.R Method Situation: Set the context. Task: Define your responsibility. Action: Show what you did and why. Result: Highlight measurable impact. Here’s one of my go-to answers: ➡️ Situation: A sales forecasting model I built was challenged by a senior manager who disagreed with my assumptions. ➡️ Task: Address their concerns and build trust. ➡️ Action: I explained the model's logic in simple terms, validated assumptions with data, and shared alternative scenarios. ➡️ Result: The manager approved the model, which improved forecast accuracy by 15%, helping optimize inventory decisions. What I learned: ✔️ Be specific, not generic. Share real challenges and measurable outcomes. ✔️ Show impact beyond the technical. How did your work (measurable impact) help the business or team? ✔️ Practice storytelling. Make your answers engaging but concise. Behavioural questions are your chance to show who you are beyond the code. For me, this shift in mindset turned interviews into conversations—and it made all the difference. What’s your approach to nailing behavioural questions? Share your thoughts below! 👇 #InterviewPrep #InterviewTips #JobSearch #DataScienceJourney #DataScienceLearnings #DataScience #MachineLearning #AI

As a Data Scientist, I get asked these ML questions all the time. Thanks to DataInterview.com and Daniel Lee for putting together this resource: https://lnkd.in/gC44Tf3r

𝗗𝗮𝘆𝟳/𝟵𝟬: 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗮𝘁 𝗘𝗫𝗟 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗰𝗲𝘀𝘀: I applied through LinkedIn’s job section. The interview process includes generally one or two rounds. 🔶 𝗥𝗼𝘂𝗻𝗱 𝟭: Technical 1 hour 🔹 Questions were focused on Project, significant questions from statistics.  🔹 Few questions based on traditional ML algorithms and model performance metrics.  🔹One SQL and one coding question was asked to code live 𝗗𝗶𝗳𝗳𝗶𝗰𝘂𝗹𝘁𝘆: Medium to Hard. 𝗥𝗲𝘀𝘂𝗹𝘁: I didn’t heard back. Keep an eye out for my next post on the Data Scientist interview at 𝗖𝗶𝗿𝗰𝗹𝗲 𝗞. #90days #learning #interviewprep #datascience #AI #job #interview

Nice summary! Mastering these hyperparameters is essential for optimizing machine learning models. Utilizing techniques like Grid Search, Random Search, and Bayesian Optimization significantly enhances model effectiveness. #machinelearning, #artificialintelligence, #datascience, #bigdata, #deeplearning.

During the interview process for a Data Science role, many companies these days start with a case study to be solved by the candidate. Case study at times may seem boring as it takes effort and time but on the positive side, the candidate gets to solve a toy version of the actual domain-specific problem the company is working on. It can be from Finance, Retail, Human Resources, Real-estate or Manufacturing domains, that you may not have worked in past and can be good learning and experience. Here's a short video for Data Science enthusiasts I have created on the topic of the ongoing data science interview process trends. Like and subscribe for more such interesting concepts. Also, like and share over here for maximum reach. : ) Video Link: https://lnkd.in/e7EKhZiJ YT channel Link:  youtube.com/@datatrek #datatrek #datascience #machinelearning #statistics #deeplearning #ai

🎯 Master AI/Data Science Interviews: Top 4 Essential Skills You Need 🎯 Want to ace your next AI or Data Science interview? Here are the key skills you must master: 1️⃣ Technical Depth – Showcase your expertise with in-depth knowledge of AI and data science tools. 2️⃣ Business Acumen – Understand how AI solves real-world business problems. 3️⃣ Problem Solving – Think critically and approach challenges with innovative solutions. 4️⃣ Communication – Clearly explain complex concepts to both technical and non-technical audiences. 💡 Ready to develop these skills? Join our courses and become interview-ready! 💸 Fee: INR 199/- 🔗 Sign up link: https://lnkd.in/dAYwTTXa 🗓️ Date: 28th September, 2024 📞 Enquires at: +91 9148398744 #datascience #machinelearningtools #machinelearningalgorithms #decisiontree #decisiontreealgorithm #machinelearning #mlopsengineer #datasciencecourse #datasciencecommunity #datanalytics #machinelearningmodels #data #dataanalysis #mlopscareers #mlopsjobs #machinelearning #machinelearningcourse #genai #artificial_intelligence

The 30 interview questions you need to know before your next data science interview: I created this list using You.com, which has become my AI sidekick for literally everything in my life, from research, to trip planning, to everyday questions. Want to take this to the next level and tailor this toward the actual position you’re interviewing for? Copy and paste the description for the position you’re interviewing for into YOU.com. Then ask YOU to generate statistics, machine learning, programming, etc. questions based on that description. And then voila, you have a TARGETED list of interview questions to practice from. Try out YOU.com completely free for your next data science interview using the link in comments 👇 ♻️ Reshare this for aspiring data scientists! #sponsored