Christopher Blood’s Post

Are you a Data Scientist on the hunt for something new in 2024? Here are 3 things you NEED on your CV (Number 3 is obvious, but often missed out 😬) 📄 1. Information SPECIFIC to the role you are applying for. The temptation is to show off everything you've ever done but remember, the person reading your CV might not have the technical knowledge to understand it 👀 If the job description requires experience in clustering algorithms and you list K-Means, the person hiring might not know that they're the same thing and assume you don't fit the criteria. Your CV gets chucked in the discard pile all due to miscommunication 🗑️ ⭐️ Tip: Make sure you're including evidence of your experience that directly aligns with the job description (word for word) 📄 2. Don't assume they know what you're doing now Just because you have a skill listed in a previous role, doesn't mean that the recruiter will assume you're still using it now. Many recruiters, when looking for data scientists, will be hunting for recent relevant experience. ⭐️ Tip: Even if you think it's obvious that you're still XYZ, list it! 📄 3. Data, data data Wherever you can, back up the points on your CV with real, solid data. Demonstrate the achievements you made, and how this benefitted your team and the company - especially time and/or money you saved. ⭐️ Tip: Show, don't tell! Remember, at this stage, it's not necessarily the best candidate who gets chosen, but the best CV. Hit all 3 points above and you're in with a better chance of getting to the interview stage where you can prove your credibility. 🔎 TLDR; Don't assume that the recruiter knows everything about data scientists. Use laymans terms and, wherever possible, hit the key points in the job description. Want to be first in the know for new data science roles next year? Drop me a message today and let's chat about your goals for 2024 📥

Have you ceased applying for positions? What would be the benefit of discontinuing your applications for data roles? Who conveyed to you that it would be effortless? Acquiring valuable things can sometimes be challenging. Why would you abruptly quit before obtaining them? Was it anticipated to be straightforward? I haven't encountered any indication suggesting that landing data roles is easy. If you have, I'd appreciate it if you could share it with me. Moreover, do you realize it's not merely an individual endeavor but rather a competitive one? How can you then anticipate it to come effortlessly? Even if it doesn't come easily, strive to obtain it and exert all efforts to retain it and continually progress in it. It may eventually become more accessible, and if you acquire it easily, assign value to it. Engaging in data work is rewarding, yet securing the job can be demanding. Don't be disheartened, so you don't miss out. Go for it, you're getting it 💪💪💪 #data #datajobs

As we approach Q4 the jobs market in data is certainly improving with a lot of vacancies popping up on my job alerts on a daily basis... The general sense I get at the moment is that clients are more stringent with their hiring budgets whilst candidates need to feel a business is financially stable & will offer them stability and alignment with career aspirations. Making sure there is a fit culturally, and that the right soft & analytical skills are there in the right candidate is key for clients right now. That is why I try where I can to meet line managers as well as talent partners for all the roles I work so I can perform a deep-dive of the requirement before I work a role. I firmly believe that a well qualified brief sets a consultant up for success and empowers recruiters to find the right candidates for their clients! With that said - I'm working on a number of roles right now which have come through: - Senior Data Engineer - 60k DOE - 1 day in London per week; strong emphasis on SQL, and being a project delivery lead over a data platform. - Senior Analyst - 45K DOE - 1 day in Leicester per week; individual contributor position in T-SQL & Power BI for a digital marketing agency, providing insights and analysis to clients in retail. - Lead Analyst - 55K DOE - 1 day in Leicester per week; suited to a senior BI professional in database marketing, who will act as a conduit between clients & a delivery team, managing a team of analysts to deliver insights to clients. - Data Analyst - 35k DOE - 2 days in Birmingham per week; working in tandem with a Head of Data Strategy to enable best practices in Data Quality & Governance whilst providing insights & reporting to internal stakeholders. For more details on the above drop me a message OR apply via the links on my profile! #data #recruitment #analytics #SQL #python #movement8 #jobsindata

A data analyst plays a vital role in a business, especially during this digital age. Get to know more about their role and the reasons why you need to hire them! 👩🏼💻 Read here: https://lnkd.in/g4uCDHd4 #OneCoreDevIT #CoreBlog #CoreInsights #DataAnalyst

This is the state of the UK Data Jobs market as of this morning: 🔷 12,000 + data analysts looking for work (LinkedIn) 🔶 1032 live jobs advertised Of the 1032 data analyst jobs advertised, many will be duplicates, I feel like the real figure is closer to 800 in all honesty. That means that to get an analyst job, you need to be: 1) In the top 8% of your field. 2) Or be 12x better at standing out than your competition. Networking and professional branding is so important. #data #datarecruitment #datajobs

#Hiring role for Data Analyst for Position in North Chicago, IL (3 Days onsite per week). A data analyst's main responsibilities include collecting, processing, and analyzing large datasets to extract meaningful insights. They create reports, use statistical methods to identify trends and communicate findings to help organizations make informed decisions. Additionally, data analysts often contribute to data quality assurance and play a key role in optimizing data-related processes. Responsibilities: Developing and maintaining databases, and data systems – reorganizing data in a readable format. Performing analysis to assess the quality and meaning of data. Filter Data by reviewing reports and performance indicators to identify and correct code problems. Using statistical tools to identify, analyze, and interpret patterns and trends in complex data sets could be helpful for the diagnosis and prediction. Assigning numerical value to essential business functions so that business performance can be assessed and compared over periods of time. Analyzing local, national, and global trends that impact both the organization and the industry. Preparing reports for the management stating trends, patterns, and predictions using relevant data. Working with programmers, engineers, and management heads to identify process improvement opportunities, propose system modifications, and devise data governance strategies. Preparing final analysis reports for the stakeholders to understand the data-analysis steps, enabling them to make important decisions based on various facts and trends. Scrutinize data to recognize and identify patterns. Use data modeling techniques to summarize the overall features of data analysis. Using automated tools to extract data from primary and secondary sources. Removing corrupted data and fixing coding errors and related problems. If you are interested please DM Eshant Sharma Only US Citizen or Green Card Holder OR H4 EAD L2 EAD OPT EAD and CPT EAD  Client will not sponsor any visa  No H1b or 3rd Party #DataAnalyst #DataCleaning #DataAnalysis #StatisticalAnalysis #DatabaseManagement #Dashboards #Reports #PowerBI #DataVisualization #Tableau #Matplotlib #Seaborn #BusinessObjects #Excel #SPSS #SAS #SQL #Chicago #NorthChicago #Illinois #Hybrid

Don't fall into the trap of making this BIG mistake as a data analyst starting out in 2024. What mistake is it? ⏩ Over glamorizing the profession! The truth of the matter is, no one can genuinely explain to you the countless number of midnight candles you have to burn in order to understand that "4 hours long" course on DataCamp when it actually takes you 10-12 hours. Or the moments of writing seemingly correct SQL queries only to rewrite again at the last minute because of a "minor" mistake that shouldn't have been overlooked. Or easily forgetting a syntax you literally understood 45 minutes ago. The real test of your resilience here is your underlying "why" you want to be an analyst - the high high end salaries, remote work opportunities, prestigious titles and other glamorous perks you can think of are the trophies for your hard-fought battle, not the starting line. Keep your focus sharp or prepare for a dizzying run in circles. The choice is yours. It's your call! -------------------------------------------------------- Hi, my name is Princilla, a self taught data analyst who understands how difficult it can be as a newbie just starting out in the field of data. Follow me for more content like this that eases such a burden of your shoulders And REPOST this to have someone in your network see this! -------------------------------------------------------- #data #dataanalytics #businessintelligence

My friend lost a data analyst role in the final round. It was a perfect role in his choice organisation. When he reached out for feedback, the recruiter told him he and another candidate made the final interview. But they hired the other candidate because they had "domain" knowledge. - Remember to improve your domain knowledge as you look for your next data analyst role. Domain knowledge is understanding a particular industry, sector, or business area to "speak their language" and interpret data meaningfully. You can acquire this knowledge by reading if you don't have a formal background. Recruiters will always prefer candidates with technical skills AND domain knowledge. Don't leave it until the final round and lose out on a great opportunity. Start reading. - Interested in data analytics? Get my free data starter pack alongside 3,000+ analysts and get started immediately. The link is featured in my profile. Follow Banji Alo for more actionable advice. #data #dataanalytics #careeradvice #careers

The data science/data analyst communities are full of talk about how the market is oversaturated. People keep citing the high application numbers on online job boards. 200 applicants. 400 applicants. 4000 applicants. A friend of mine recently applied for a remote data analyst position that had over 4000 applications. The job posting promised that they would review every single application, so he at least had a chance. Amazingly he got the job. No internal connections, just a submitted application and two rounds of interviews. When he began working and the time was right, he asked the hiring manager how they were able to sort through 4000 applications. To my friend's surprise, the hiring manager said and I quote: "Dont take this the wrong way, but there were literally only two people out of all the applicants that were qualified for this job." Stunning. This manager had gone through 4000 resumes and in his mind, 3998 of them were not qualified let alone stand-out candidates. My friend went all out with his application. The application asked two analytical questions and he made entire Jupyter notebooks with visuals to support his answer for each one. In phase two of the interviewing process, they asked another set of analytical questions but this time provided their own in-house data. He made another set of notebooks with in-depth responses, one-pagers, and a lucid flow chart to make sure his point was understood. It is hard to imagine anything more he could have done in his application. And he got the job. Not all companies will review each application, so there is a serious risk in putting a lot of time into an application that no one will see. But if you can make sure they see it, you are not actually competing with 200, 400, 4000 people. You really are competing with just a couple who put in a similar effort. In a world of quantity, if you want to stand out you really need quality.

Tech stacks change How you solve business problems won't Get good at doing the latter → Truly understand the problem → Ask questions → Figure it out → Rinse and repeat This is what they call experience And this is what companies want to hire -- Notice how it's not "years of experience" That's because you can create your own experience before you are a Data Analyst Solve problems/questions with your projects