Hilary Mine’s Post

Project leads and managers, do recognise a “Death March” when you come across one. Only solution in my opinion: manage risks and issues as they arise, and manage delivery expectations. Acknowledge when you have made a mistake or over-promised. Reduce scope, extend deadlines, or scrap the whole thing. #projectmanagement #expectations #goodpractice #burnout #raisingawareness

For people currently job-hunting on the #datascience or #dataengineering market or others who have already gotten hired, what is your experience with take-home technical assignments? How much is too much? What types of questions do you think are the best way to show what you’re capable of as a data scientist or engineer? What format is ideal? What hasn’t worked? I do feel the field has historically never gotten this part right and curious to hear thoughts on the best way to do technical interview with future candidates.

No, hiring 10 additional junior team members will not address the issue. We are developing sophisticated data products, which require skilled developers and data scientists. This is more like crafting a Rolex than constructing a house that merely needs manual labor Precision and expertise are crucial, not sheer numbers. #operationsresearch #computerscience #algorithms

Data Engineers😎: 50% of the Time Building the Data Pipeline, and the rest 50% of the time thinking about moving to Software Engineer or Data Scientist. Software Engineers😊: 50% of the Time Building the Application, and the rest 50% of the time thinking about moving to Data Engineer or Data Scientist. Data Scientist😎: 50% of the Time Building and Training the Models, and the rest 50% of the time thinking about moving to Data Engineer. Data Analyst: 50% of the Time Building DashBoards, and the rest 50% of the time thinking about moving to Data Engineer or Data Scientist. At the everyone like 🤔does AI replace My Job? What do you think apart from your actual work? #Tech #coding #humor

Statsig is growing fast, and we are hiring! This is the JD for senior data scientist, Timothy Chan is the hiring manager: https://lnkd.in/gga6BkPe This is the JD for engineers, Marcos Arribas is the hiring manager: https://lnkd.in/gvKtAJre And this is JD for account executives, Sam Maher is the HM: https://lnkd.in/gw5JEPxh But wait! Before you leave a comment or DM them that says "Interested", I highly encourage you to take a look at this video, about how to become the 2% of the applicants who are actually being considered: https://lnkd.in/gGXpp6P6 If you want to get the hiring managers' attention. I advise you to seriously consider at least three questions 1. What are their non-negotiable requirements, and do I fit? 2. What's their working style, and do I like it? 3. What's their mission, and do I agree? Start with the answers to these three questions. You will go a long way.

It’s always the game of mindset. A good data scientist should also be a good product owner.

Junior Data Scientists: "That's my model." Data Scientists: "That's my analysis. I believe there are some challenges with one of our products." Senior Data Scientists: "That's the data and insights. I believe we should focus more on product 'X'; the users' behavior shows that we have bigger potential there." Staff Data Scientists: "That's the data and analysis. Let's focus on product 'X'. I suggest running this experiment for this segment of customers; we can expect a 'Y' uplift. Let's gather everyone together tomorrow with software engineers and come up with a solution on how to implement it." You're right; the difference isn't in the technical skills.

When you're hired as a Data Scientist, you might end up doing engineering work most of the time. This can be frustrating, especially if you were expecting to use your data analysis skills more frequently. However, there are ways to make the most of this situation. By collaborating with engineers and learning from them, you can become a more well-rounded Data Scientist. #dataengineer #dataengineering #programmerhumour #codinghumor #justunderstandingdata

When you're hired as a Data Scientist, you might end up doing engineering work most of the time. This can be frustrating, especially if you were expecting to use your data analysis skills more frequently. However, there are ways to make the most of this situation. By collaborating with engineers and learning from them, you can become a more well-rounded Data Scientist. #dataengineer #dataengineering #programmerhumour #codinghumor #justunderstandingdata

The 1st Data Scientist. It's the hardest hire. They have to bring so much more to the table than a regular Data Science hire in an established team. Consider what the 1st Data Scientist needs to take on: - Build great relationships with colleagues and Execs that might not have worked with DS before - Get access to the right Data - Consider data Infrastructure & tooling they have to work with - Prioritise use cases & projects, based on their potential value + the data available to work with. A complex commercial equation. - Present insights to leaders - And ultimately they’ll need to develop prototypes and tools which actually deliver value. Oh, and you’ll probably also need to be a Data Engineer a lot of the time as well 😊 I’m telling you though, for the few able to pull it off over a 12 – 18 month period, you’ll be worth your wait in gold. Because most in the space can’t deal with this level of autonomy & blank canvas. #datascience #dataengineering #datastrategy