Sennet S.

Summer internships have just started, and if you're looking to get a return offer from your current employer, this post is for you. Now, you might think it's way too early to be thinking about this since you just started, but 4 months go by fairly quickly, and in this market, there's no such thing as being too early. Following my 1st internship with DRW, I decided to join them full-time. Luckily for me, though, I had a great manager watching out for me, who encouraged me not to be too hasty with a decision as big as this one and to do another internship with them before making a final decision. For my 2nd internship, she gave me a taste of what it would be like to work there full-time, so I worked on more challenging projects, presented more at team meetings, etc. Honestly, this was the best suggestion I could've gotten. So, 1. Just like my manager suggested, don't be too hasty in making a decision. 4 months is a pretty brief period to get to know a company, so see if you can do multiple internships at the same company, work on different teams, etc. 2. Be transparent. The first thing I did in my first week was to sit down with my manager and share that my goal was to get a return offer. Now, you may get a response like 'we don't have the budget' or 'we can't promise you anything.' That's fine. That's beyond your control. But what is within your control is your performance and work ethic. Logistics and budgetary restrictions aside, you should know what is required to get a return offer. Since I wanted a return offer, I requested to be assigned projects where I could show the most impact. Set frequent check-ins so you know you're on track with expectations. 3. Network! Network with your co-workers, with folks from other teams, grab coffee chats, etc. You want to cast as wide a net as possible. In this market, it's very important to keep realistic expectations, so despite you doing a great job, your employer might not have the resources to offer you a full-time role. You have to be prepared for that. Networking can open up more doors for you in case this one doesn't work out. You never know who, in addition to your manager, might give you a vote of confidence which ends up tilting the odds in your favor. 4. Keep a plan B. Even if they offer you a full-time role, having other offers buys you leverage, which is an incredibly powerful tool. Keep applying to other companies and stay on top of your interviewing game (I talked about this in my previous post). I've talked extensively about things like your technical proficiency, communication, etc, in previous posts - they're linked in the comments. If you have any further questions about your return offer, feel free to drop them in the comments below or DM me! #softwareengineering #careers

145

11 Comments

For week 7 of CUNY Tech Prep, let’s talk about… week 8! We’re learning about decision trees and random forests! Trees should be a familiar concept to us computer science students, and indeed, they appear to be the data structure that serves the purpose of classification by branching paths. Decision trees unfortunately suffer from overfitting, and that’s not hard to see from how dependent they are on the data they are built from. Thankfully, random forests mitigate this problem by introducing an element of flexibility by having a bunch of decision trees to make classifications instead of just one! Random forests use a strategy called “bagging”: we bootstrap the data by randomizing a subset of it, and then aggregate the classification results of the random forests. We can evaluate the accuracy of the random forests by using data not used for the forests: called “out-of-bag” data! While we initially compare two columns per split, we can try multiple columns per split and repeat this process until we have the most “pure” classifier. Excited to learn more and experiment with decision trees and random forests this Tuesday!

6

A lot of young, fresh talent—whether they’re grad students or just starting their careers as software engineers—often ask me, "How can I become better? What should I learn, do, etc.?" My answer is simple: Keep asking yourself that question. Having the mindset and attitude of constantly striving to improve is exactly what makes you better over time. As for me, what makes me better? Honestly, it’s that I just love what I do. When you’re passionate about your craft, it’s natural to keep growing and getting better. So, if you’re looking to improve—make curiosity, passion, and a love for learning your foundation. It will take you far.

8

Over the past few weeks, many people have reached out to me to learn more about the work I did during my internship at American Express, where I focused on 𝗲𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝘁𝗵𝗲 𝗰𝗮𝘂𝘀𝗮𝗹 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝘂𝘀𝗲𝗱 𝗯𝘆 𝗹𝗶𝗻𝗲 𝗮𝘀𝘀𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗺𝗼𝗱𝗲𝗹𝘀. To share my technical insights, I’ll be detailing my experience in a series of posts, starting with an important concept I worked on: the distinction between predictive and causal modeling. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝘃𝘀. 𝗖𝗮𝘂𝘀𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: 𝗪𝗵𝘆 𝗖𝗮𝘂𝘀𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗶𝘀 𝗡𝗲𝗲𝗱𝗲𝗱 In data science, 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 is widely used to forecast future outcomes based on past data. For example, a retail company might use a predictive model to estimate next month’s product sales using historical trends, seasonality, and customer behavior. While useful, this approach only tells us 𝘸𝘩𝘢𝘵 𝘮𝘪𝘨𝘩𝘵 𝘩𝘢𝘱𝘱𝘦𝘯 but not 𝘸𝘩𝘺 it happens. Enter 𝗰𝗮𝘂𝘀𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴, which goes a step further to uncover the cause-and-effect relationships behind the outcomes. Instead of just predicting future sales, causal modeling answers questions like, 𝘋𝘪𝘥 𝘢 𝘳𝘦𝘤𝘦𝘯𝘵 𝘮𝘢𝘳𝘬𝘦𝘵𝘪𝘯𝘨 𝘤𝘢𝘮𝘱𝘢𝘪𝘨𝘯 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘤𝘢𝘶𝘴𝘦 𝘵𝘩𝘦 𝘴𝘢𝘭𝘦𝘴 𝘣𝘰𝘰𝘴𝘵, 𝘰𝘳 𝘸𝘢𝘴 𝘪𝘵 𝘥𝘶𝘦 𝘵𝘰 𝘰𝘵𝘩𝘦𝘳 𝘧𝘢𝘤𝘵𝘰𝘳𝘴? 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿? Predictive models might show a correlation, but correlation doesn’t equal causation. If we want to make data-driven decisions—whether it’s in marketing, healthcare, or finance—we need to know what actions directly influence outcomes. For example, knowing that a campaign caused an increase in sales is far more valuable than just predicting sales trends. In my next post, I’ll discuss different causal algorithms, including the ones I used in my project. Stay tuned and follow for more! #internship #healthcare #finance #machinelearning

90

6 Comments

Great tips about what to expect during the SWE interviews at Bloomberg!

9

These are some great tips, especially using a tool like Pramp to practice interviewing. I think once you have the foundation, you get good at interviews by doing more interviews, not by doing more problems.

8

Wondering how our entry-level interview works? Check this out!

14

Matt went from "Open to Work" to "Thrilled To Announce" in 3 weeks (he had more than 1 offer). My advice to those still looking after many months - you need to take a pause, do some self-reflection and improve something. To put it bluntly - something is wrong (unless you're looking for high level leadership, then good luck...companies needs more rowers than steerers). Hiring slows down around now, and now is a good time for the self-reflection. The election uncertainty is over and companies will adjust budgets accordingly. Jobs should open up in Jan. Don't be caught flat footed, again. A lot of people were laid off from GM in the last few weeks & months. It was emotionally taxing for me to see waves of layoffs & "mutual separation". Despite having left GM 1.5 years ago, for some, I was the first person they contacted. I'm very thankful people have this trust in me. Being a mentor and a people leader is something I'm passionate about. I voluntarily reviewed resumes, conducted mock interviews, provide motivation, general life advice. I'd spend my nights and weekends juggling appointments. I'm happy to say, with Matt's new job, everyone I've worked with who took me seriously are now employed with similar or better jobs. My phone should be quieter...at least, till Feb? The job market is tough, but there's still a market.

43

6 Comments

Meta released their New Grad software engineer and 2025 summer internship job postings! Here they are: New Grad - https://lnkd.in/e6KcE-wX Summer 2025 Internship - https://lnkd.in/e_9yMFyR Meta University Internship (for Freshman / Sophomores) - https://lnkd.in/edA9Ewwy I’ve been loving my time at Meta and highly recommend it. Here you work with, learn from, and are mentored by the best engineers in the industry. Hurry and find someone who can refer you and then get your application in ASAP! Heads up, feel free to reach out to me to ask for a referral, but with the large number of requests and limited referrals I can give, I can’t promise anything. But I’ll do my best to help in other ways! One of the ways I can help is by sharing spreadsheets I am maintaining that are full of internship and new grad opportunities at companies that would be at the top of my list if I was recruiting right now. The spreadsheets are in the same template that I used to track my application progress when I was recruiting. They also include some helpful tips on how to secure referrals. Send a connection request and comment “interested” on this post if you want me to dm it over to you. Good luck out there. #SoftwareEngineer #coding #recruiting

144

494 Comments

Hello LinkedIn Friends, I’m excited to introduce you to Nolan Maske from NCA&T University. Nolan and his team won first place in the Fall 2024 Hackathon with Goldman Sachs! Even more impressive is the application they developed to win the competition. They created a web app called PredictFi, designed to predict future earnings for financial companies. Congratulations, Nolan and team! If you follow my posts on LinkedIn, you know that Market Leader Game (MLG) also predicts future earnings. At first glance, you might think PredictFi and MLG are similar. Both use statistical modeling to forecast earnings. For example, MLG’s algorithm identifies NASDAQ or NYSE stock symbols that are most likely to experience significant price increases over the next 12 months. @Nolan, it would be fascinating to learn more about the context and objectives behind future earnings predictions in PredictFi. MLG selects stock symbols with the highest probable growth rates by combining and then comparing: - Monte Carlo-generated stock prices, - Actual growth rates from the past 12 months, and - Detailed analysis of income statements and balance sheets. I purchased 3 stocks in 05/24 and 2 more in 10/24 to test the model. The performance (as of today) has been remarkable - 148% growth in 6 months: The difference between MLG and PredictFi became clearer when I asked Nolan if his team used another statistical model to derive future earnings. Nolan replied (paraphrasing): "PredictFi uses ARIMA (AutoRegressive Integrated Moving Average). The application assumes past patterns in data will continue. The team did not have sufficient time to explore the uncertainties and random outcomes that Monte Carlo could address." His response piqued my interest, leading me to dive deeper into the differences between Monte Carlo Simulation and ARIMA. Here’s what I found: ARIMA Modeling Strengths: Time-series focus: Well-suited for capturing trends, seasonality, and autocorrelations in stock prices. Deterministic forecasts: Based on past patterns and relationships within the data. Simpler assumptions: Assumes that historical patterns will persist. Monte Carlo Simulation Strengths: Flexibility: Models multiple potential price paths using randomness and volatility. Captures stochastic behavior: Reflects the uncertainty and randomness of financial markets. Use Case: Best for long-term risk assessment, scenario analysis, and when uncertainty and volatility are key factors. Conclusion Use ARIMA for short-term forecasting and when historical trends dominate. Use Monte Carlo for long-term forecasting, risk assessment, and probabilistic scenarios. Combining these methods or integrating them with other models (e.g., machine learning or GARCH for volatility analysis) often yields the best insights. I hope this comparison sheds some light on these powerful predictive tools. Congratulations again to Nolan and his team for their impressive achievement!

8

🌟 𝐃𝐫𝐞𝐚𝐦𝐢𝐧𝐠 𝐁𝐢𝐠, 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐁𝐢𝐠𝐠𝐞𝐫 🌟 Last May, I embarked on an incredible journey with WisdomAI right after graduating from UIUC, and it has been nothing short of a dream run! Starting my career with a passionate, dynamic team like this one has been an amazing experience. 🚀 Here’s what I’ve absolutely loved about my time at WisdomAI so far: 1️⃣ Ownership Sparks Passion When you’re empowered with the right motivation and a sense of ownership, the task transforms into a pursuit. I’ve cherished the freedom to take charge of my projects, driven by curiosity for achieving results rather than just deadlines. 2️⃣ Culture of Growth & Impact I’m deeply grateful to the founders and my team for cultivating an environment of insane growth while delivering great value to our customers every single day. Huge thanks to the founders - Soham, Sharvanath, Kapil, and Guilherme for fostering this environment. 3️⃣ The Power of Team Energy The motivation and drive of the people around you can make all the difference. Being surrounded by such an energetic, supportive team has truly set the trajectory for my career. As we step into this new year, we’re doubling down with more energy, sharper focus, and even bigger goals for our startup. Here’s to a year of innovation, impact, and scaling new heights together! 💡🌍 #StartupJourney #GrowthMindset #WisdomAI #Teamwork #Innovation #NewYearGoals #CareerJourney

136

7 Comments

🌟 Attention Boston Area Students Passionate About AI! 🌟 Are you intrigued by Artificial Intelligence, Machine Learning, or Data Science? Want to drive innovation at your campus? Let’s make it happen together! 🚀 During my time at University of Massachusetts Amherst, I founded the UMass Amherst Data Science Club and was President for 2 years. Not only did I enjoy it, but it opened up so many doors for me with things like job opportunities, industry networking, etc... 🤝 I’m collaborating with AI expert Paul Baier to compile a list of student groups relating to AI and data science in the Boston area and we’ve noticed some schools still on the sidelines without any dedicated groups in this thrilling field. I would really love to help any student at schools such as Bentley University, Suffolk University, UMass Boston, Emerson College, Brandeis University, and Boston College in setting up their first Artificial Intelligence (or similar) club! 📝 Interested in getting involved or starting a group at your school? Just drop a comment below or send me a direct message! 📊 Know of an existing group that's not on our list? Let us know! We aim to connect and empower student communities across Boston. Let’s innovate and lead together in AI! #BostonAI #Innovation #DataScience #MachineLearning #AICommunity #UMassAmherst #Boston #AI #ArtificialIntelligence #ML #MachineLearning #DataScience #AIClub

2

Thrilled to kick off the next chapter of my data science journey at the University of Pennsylvania’s MSE in Data Science program! I’m beyond grateful to have received offers from other amazing programs, including Yale University, Carnegie Mellon University, and at Penn State University with the Chancellor’s Scholarship as well. After a lot of consideration, I chose Penn for its incredible interdisciplinary coursework, which perfectly aligns with my passion. This program allows me to build on the technical skills I developed during undergrad and apply them to exciting, real-world challenges. I’m especially eager to explore how data science impacts diverse fields, like medicine (through computational biology), finance, and marketing. I've always believed that mastering technical skills is only half the journey—understanding their real-world applications is where the magic happens! With courses like Statistics for Data Science, Machine Learning, and Models in Marketing, I’m covering all the bases: from deep mathematical foundations to cutting-edge applications. Can’t wait to see where this next adventure leads! A massive thank you to all my professors, especially Prof. Amit Nerurkar, Prof. SANJEEV DWIVEDI, Prof. Dr. Sachin Bojewar, and Prof. Snehal Andhare. I would also like to thank my friends Hetavi Gala and Trisha Nimkar for constantly supporting me for the past few years. You’ve all been a huge part of this journey! #DataScience #UPenn #IvyLeague #MachineLearning #Finance #Marketing #RealWorldImpact #Engineering

239

47 Comments

Hi Everyone, In the midst of job and grad school applications, I've been pretty busy! I've made a lot of improvements to my website, https://lnkd.in/emgK8sx4! Next time you visit my site, you'll see a few new features! - Login with Clerk: You can now sign up for very infrequent updates regarding my career, new blog posts, and website development with Clerk.com! It was a lot of fun setting this up and I think it looks pretty cool. - Tagged Blog Posts: Additionally, you'll be able to navigate my blog posts through a tagging system now! So if you only want to read my essays about a specific topic, it's a lot easier. - Automated email pipelines with Twilio SendGrid: In addition to the sign-in functionality, I set up an automated emailing pipeline sent from my domain using Twilio! Expect a few emails from @em101.prakhargaming.com. I'd love to know everyone's thoughts on development so far, as well as ways I can approve. It would also mean a lot to me if you could sign up : ) Thanks, Prakhar #webdevelopment #frontend #website #react #typescript

55

6 Comments

As part of my long-term career plan, I made the decision to come to the United States, with my primary goal being a transition into data scientist and data analyst positions. Recently, I've spent considerable time contemplating which projects would be both engaging and meaningful. Today, while exploring Perplexity's U.S. Election 2024 Hub, I had a moment of clarity about how I could channel my efforts and concerns into creating something truly impactful. What struck me most about this dashboard, created by Perplexity - a platform that has already surpassed Google in terms of search utility - wasn't its 30-second real-time updates, RAG implementation, or even its hallucination-minimizing real-time data integration. What truly captivated me was the Voter Guide feature. The simplicity of entering a postal code to access comprehensive information about local candidates (Senators, Representatives, Mayors, and key political figures) was remarkable. Even more impressive was how a simple click on any candidate's name revealed detailed insights into their background, current platform, proposed bills, and policies. Reflecting on my voting experience in Korea, I realize with some embarrassment that I never had such detailed access to information about candidates, their party's policy contributions, or the backgrounds of my district's representatives. I strongly believe many American voters faced similar challenges before. While I can't speak to Perplexity's exact intentions in creating the U.S. Election 2024 Hub, from a user's perspective, it stands out as an incredibly valuable and user-friendly product. This experience has reignited my aspirations, both as an aspiring data scientist and a potential future entrepreneur, prompting me to deeply consider what kind of products I might create that could have such meaningful impact. It's reminded me that technology should not just demonstrate technical prowess but should serve to make complex information accessible and meaningful to everyday users. Below is a brief summary of our research for Perplexity U.S Election Dashboard: Data Sources and Integration <Key Data Providers> - Associated Press (AP): Receives real-time election data from AP to track vote counts. - Democracy Works' Elections API: Obtains information on voting requirements, polling station locations, and operating hours. Technical Architecture <Search and Summarization System> - Retrieval-Augmented Generation (RAG): Utilizes RAG technology to identify relevant information and generate summaries tailored to user queries. - Minimizing Hallucinations: Reduces the risk of hallucinations by relying solely on real-time data instead of the stored knowledge of pre-trained models. <Real-Time Data Processing> - Immediate Updates: Enables instant vote count updates through AP's real-time data feed. - Curated Reliable Sources: Uses data exclusively from curated, trustworthy sources like Ballotpedia and verified news organizations.

9

I'm grateful to have had the opportunity to travel back to dear NYC for a couple of days and present about some fundamental theoretical components of the Aerospike vector database. Thank you Microsoft and PyData NYC for hosting and putting on this event, and thank you Arthur Anderson for your wonderful portion of the presentation! (Side remark: after the presentation, I was absolutely stoked when someone told me that I should be a professor after college 😄) I'm taking advantage of this post to provide a few small updates: Going forward, I'll be working on more software engineering tasks with the Aerospike Graph team, and I'll simultaneously learn more about distributed systems, get deeper into algorithmic design & analysis, and spend more time with C++. I plan to post something more reflective about my gap semester experience and what I have learned , but here is a list of some other things that I have enjoyed recently outside of my internship: - 📓 Reengaging with my Applied Mathematics courses in nontraditional ways. Without exams to study for, I've taken full advantage of this time to relearn a slew of topics from original principles, and this has been a tremendously fruitful and satisfying process. Additionally, considering the context in which mathematical concepts were discovered has boosted my appreciation for my interests. I highly recommend "The History of Mathematical Proof in Ancient Traditions" by Karine Chemla if the math-history intersection intrigues you. - 🥾 Getting outdoors more often. Whether it's walking my dog, running, or backpacking for a couple of days, I've been making a conscious effort to rejuvenate my mind and avoid any shred of mental stagnancy at all costs. Side note: I saw my first barn owl last night (indeed, I love birdwatching). - 📚 🇫🇷 Cultivating a couple of daily habits. I have made doing (duo-ing?) my Duolingo lessons a priority, and I now realize that my years of Latin are finally paying off with other European languages. I've also made daily reading a priority, and I have set a fairly modest goal of one book a month. I'm right on track so far! I'm very proud of (1) my work and (2) my work-life balance thus far, and I'm absolutely looking forward to the upcoming academic year. There are various reflections that I've unearthed over the past 6 months or so, and I certainly plan to articulate them here towards the end of this summer!

45

1 Comment