Garner Health’s Post

Building a strong data team is difficult, especially in the current tech job market. Whether you're hiring software engineers, data scientists, or developers, the average tenure is only about two years or less. Shockingly, around 69% of technology hires leave within the first two years. This poses a significant challenge for companies. Want to learn how to build a strong data team? Watch the full video here: https://lnkd.in/gzfXFwXZ #datateam #datamangement #dataanalytics

Aleut Federal is #hiring a Data Scientist III This Hybrid opportunity is located in the Washington, DC office. 🎇 👀Data Analysis & Interpretation: Analyze large, complex datasets to uncover trends, patterns, and insights that inform strategic decisions. 👉Model Development: Develop and implement machine learning models, algorithms, and statistical techniques to solve complex problems. 👉Data Management: Clean, preprocess, and organize data from diverse sources ensuring data integrity and accuracy. 👉Collaboration: Work collaboratively with other data scientists, analysts, engineers, and stakeholders to understand requirements and deliver impactful solutions. 👉Visualization: Create clear, concise, and compelling data visualizations and reports to communicate findings to non-technical stakeholders. 👉Research & Innovation: Stay abreast of the latest advancements in data science, machine learning, and artificial intelligence to continually improve methodologies and processes. 👉Security Compliance: Ensure all work complies with security protocols and maintains the confidentiality of sensitive information. Apply Here ⬇ https://lnkd.in/ep6SqWrV John L. Papp, Jr. & Allison Mengel

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.

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

Spot the difference, Data Science hiring edition: Approach 1: - Entire tech stack on JD - Obsess over degree & years of experience - Critique Software Engineering skills on the whiteboard - Complain about lack of good candidates Approach 2: - Outline the needs & motives of DS in the role & company - Obsess over value-add & growth potential - Collaboratively assess problem-solving & communication - Make efficient & effective hires Don't do number 1. #datascience #analytics #data #datascienceinfinity

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.

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

If you hire a data scientist before you've hired a data engineer, congratulations, you've just hired an entry-level data engineer!* *Unless they've had the same thing happen to them multiple times, in which case, congratulations, you've hired a seasoned data engineer. #datascience #dataengineering

Data is everywhere and there is an abundance of it now. In a way, we are recording the DNA of our lives every day. The important question is how do we deal with this data and transform it into actionable decisions. This is where data science comes into play. Many companies and industries are now hiring data scientists as they shift their direction and management to rely more on data-informed business decisions.

This post made me smile this morning! Most business leaders understand sales and marketing, since they usually have that background. With everything becoming “data-driven” a resource grab for data talent is ongoing. However, most business leaders have no idea how nuanced the data field actually is and “data scientist” is the sexiest title most people think of. (Well maybe not this week, AI engineer is taking the stage….which is still data dependent, btw). Hence, the painfully truthful post below. If you have data that is not where it needs to be or the shape it needs to be in, you need to start with data engineers. When the data starts to flow and has meaning, then a data scientist can work their magic. Foundation first, data application second, insights later. #digitaltransformation #datastrategy #dataengineering #digitalbusiness