Concirrus’ Post

💥 Two Data Science opportunities in Insurance / Financial Services 💥 We have partnered with a top performing syndicate to hire for their newly formed Data & Analytics team. Both of these opportunities will give you the opportunity to utilise your Data Science experience to set the organisation up for future success and have the recognition of making a real impact to business performance 🚀 More details via the clip below ⬇️ Link to apply for both positions in the comments 💭 #insurance #actuarial #london #datascience #machinelearning

Hellllllo #Network! Do you like #data? I am ALWAYS looking to talk to excellent analysts, engineers, and architects with skills in SQL, data pipelines, data science, data modeling, and analytics! I also love chatting to those that are driven to serve! If that's you, let's talk! #hiring #dataengineer #datamodeling #dataanalyst #dataarchitect #consulting

Scene+ is a data science-driven company, and their membership is growing exponentially. Influence technology choices, work with vast volumes of data from every major customer-driven industry, and enable intelligent decisions impacting their loyalty members as the new Senior Data Analyst. https://lnkd.in/d_kk5cfa #Hiring #WorkSomeWhereAwesome #Job #JobSearch #DataAnalyst #LoyaltyProgram #DataScience

Conventional data analysts, data scientists and data engineers are often not what companies need. What companies are mostly looking for are the Data professionals that bridge the gap in between: - that data analyst who also know a bit of building Data Pipelines/ETL.  - that data scientist who has good project management skills  - that data engineer who is also familiar with the business needs - Functional SME who translates business needs to technical models No matter how big the company/team, it's often these individuals that provide the most value. So as a data related professional, don't think too rigid of your role. Dare to think outside your job description and get reward for it. :) #Data 

Am I a data engineer or a fire fighter 🧘🏿 Day in the life of a Data Engineer includes 1. No matter how much work you close before the weekend, new day new problems 🎁 2. Develop queries ⌨️ 3. Share sample data for data sanity with source and business teams 💾 4. Day to day project related issues and solutions 🪛 5. Ensure all data pipelines are in place and data reaches designated teams to avoid escalation 🆘 6. Take some help from colleagues, senior management or business teams to not get stuck over a problem 🚒 7. Work on compliance, yes, that's a routine 🌇🌃 If that sounds cool, get your hands dirty. #data #dataengineering #banking #engineering #growth #workexperience

Data engineer, data scientist, and data analyst are three distinct roles within the field of data and analytics. There is some overlap in skills and responsibilities, each of these roles plays a unique and important part in the data ecosystem, with distinct focuses and objectives based on their expertise and technical capabilities. #dataEngineer #dataScientist #dataanalyst

I've been hiring for data engineers lately and I've noticed that I've been getting applications from data analysts as well. Intrigued, I did some digging and found out that there's quite a bit of overlap between the two roles. Check out the image below to see what I've learned! If you're a data analyst looking to make the jump to data engineering, this might be helpful information for you. #dataengineering #dataanalytics #jobsearch Zaki El. 🎮 Venkata Ravindranath Sreepada Swarna Polamarasetti Ravi Kant Arindam Dutta

Insightful read on the evolving landscape in data careers! The shift from data analyst to data/analytics engineer reflects a deeper understanding of the critical aspects of data management. Data analysts primarily focus on interpreting and gaining insights from data, while data engineers concentrate on designing and constructing systems that collect, manage, and convert raw data into usable information. This transition underlines the importance of a strong foundation in both the interpretation and the technical handling of data. It’s a testament to the value of hands-on experience with data, highlighting the importance of quality, efficiency, and robust systems. This perspective might just be the key to unlocking new potential in the field of data engineering. #DataAnalytics #CareerTransition #DataEngineering #IndustryInsights

🤔 𝐖𝐡𝐢𝐜𝐡 𝐝𝐚𝐭𝐚 𝐣𝐨𝐛 𝐫𝐨𝐥𝐞 𝐬𝐩𝐚𝐫𝐤𝐬 𝐲𝐨𝐮𝐫 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭? 𝐋𝐞𝐭'𝐬 𝐜𝐡𝐚𝐭! 🚀 Data-driven careers come in various flavors, each a vital ingredient in the recipe of data magic. Let's explore some common job titles in the realm of data analysis, data science, and data engineering. 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭: • Responsibilities: 🔍 Data analysts decode data to uncover juicy insights and trends, fueling decision-making processes. • Example: 🛒 A data analyst in e-commerce might slice and dice customer purchase patterns to serve up irresistible product recommendations. 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭: • Responsibilities: 🧪 Data scientists brew statistical potions and whip up machine learning spells to extract insights from data cauldrons. • Example: 🏥 A data scientist in healthcare might concoct predictive models to brew better patient outcomes and treatment strategies. 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫: • Responsibilities: 🛠️ Data engineers architect and construct data pipelines, ensuring a smooth flow and sturdy storage for the data deluge. • Example: 💰 A data engineer in finance might build fortresses for processing and analyzing hefty volumes of financial transactions. #DataAnalysis #DataScience #DataEngineering #DataJobs #JobTitles #DataRoles #DataAnalytics #MachineLearning #DataInfrastructure #CareerDevelopment 📊🔬🛠️

If you're kicking off new data initiatives, don’t hire data scientist first! Based on what we've seen, it's smarter to first get on board 2 senior roles: a Data Scientist and a Data Engineer. However, if you cannot afford that, start with 0.5 FTE Data Engineer. You need to keep your pipelines running, create analytical models and ensure quality of your data collection and preprocessing process. Next up, bring in a full-time Data Analyst, this person will dive into the business needs, conduct descriptive analysis and create dashboards. You should be able to see first insights and increase transparency of your organization. After that, consider adding a 0.5 FTE Data Scientist to the mix. They'll work on proving your concepts with your data, exploring the potential of advanced analytics, and ML. Then, you should also consider having data governance expert and data lead, ideally in the proportions as shown on the picture below. It's important to note that these are responsibilities, not strictly individual roles. For instance, a 0.25 FTE in data governance could be managed by the Data Lead or Data Engineer, emphasizing flexibility in your team structure. #artificialintelligence #machinelearning #dataanalytics #datastrategy