Subreddit for actuarial professionals, students and interested (innocent) bystanders. can Ok. What type of programming is in actuarial science? I cannot really generalize to other workplaces, but I think your Get jazzed at the idea of working in different industries, or not being limited to one. To view or add a comment, sign in Anyways, while getting a masters probably won't help you become an actuary, it keeps that door open while giving you other options as well. High growth job market where your skills will be in demand. I have heard that actuaries don't use a lot of maths/stats in their jobs like data science. Unsurprisingly, the actuaries thought their career path was a better choice and the data scientists argued just as strongly for their choice. The interesting responses were the ones who had done both so had a better picture of what each career choice involved. Most of the transitions were from actuarial science to data science but there was one person who had gone the other direction. Here are their main reasons for the choices they made between being an actuary or data scientist. A good data scientist needs to have expert domain knowledge, and that expertise only comes with years of experience within that industry. He is also a self-proclaimed technician and likes repairing and fixing stuff. Actuary Yes, there is a bit of programming involved in actuarial science, but not nearly as much as in data science. They were interested in using data mining, and ML algorithms such as clustering to segment customers by demographics, and classification for better risk analysis. The actuarial science career path is feeling the pressure of big data and open source software. So, when you are planning to make a transition make sure you learn the much-needed programming language such as Java, Perl, C/C++, Python, R, SQL, etc. Having looked at the size of the actuarial candidate pool, I decided to try to estimate the size of the "data science" pool. Actuaries are good at being experts in insurance, but you can also hire someone to be an expert in insurance for a lot less. Some time ago I came across a post on my Linkedin feed titled "I'm an actuary, can I be a data scientist?" However, programming is a big part of data science, generally. You will have to do them on your own time and with your own dollar, but the credentials they give you will smooth your entry into this career. Being an actuary, you might be having almost the same set of skills and knowledge when it comes to solving problems and predicting the uncertainty. Pursue education and certification. Can you become a data scientist with an actuarial science degree? ._2a172ppKObqWfRHr8eWBKV{-ms-flex-negative:0;flex-shrink:0;margin-right:8px}._39-woRduNuowN7G4JTW4I8{margin-top:12px}._136QdRzXkGKNtSQ-h1fUru{display:-ms-flexbox;display:flex;margin:8px 0;width:100%}.r51dfG6q3N-4exmkjHQg_{font-size:10px;font-weight:700;letter-spacing:.5px;line-height:12px;text-transform:uppercase;-ms-flex-pack:justify;justify-content:space-between;-ms-flex-align:center;align-items:center}.r51dfG6q3N-4exmkjHQg_,._2BnLYNBALzjH6p_ollJ-RF{display:-ms-flexbox;display:flex}._2BnLYNBALzjH6p_ollJ-RF{margin-left:auto}._1-25VxiIsZFVU88qFh-T8p{padding:0}._2nxyf8XcTi2UZsUInEAcPs._2nxyf8XcTi2UZsUInEAcPs{color:var(--newCommunityTheme-widgetColors-sidebarWidgetTextColor)} Health Actuary vs Data Scientist: What is the difference? Silicon Valley probably offers way more data science opportunities than it does actuarial ones, so I would imagine the demand/supply ratios will be far nearer to each other than at an aggregated US level, There doesn't seem to be much difference in the demand/supply ratio in India, and I would imagine that the competition level for pre-Fellow actuarial jobs is broadly similar to competition for data science jobs; there are very few Fellows in India compared to the overall actuarial population and so Fellowship there confers a certain level of prestige that's hard to match in other industries, My analysis ignores the "intellectual challenge" aspect of work which is a key component that many people will say they consider. Incidentally, for anyone interested, the original article can be found here (https://goo.gl/Ri3ZAd), and I was particularly intrigued to see the "data science" question also come up at the UK Institute and Faculty of actuaries ("IFoA") annual general insurance conference in Dublin last week (nicknamed "GIRO"). WebAnswer (1 of 6): Some may disagree but I largely believe actuaries is no different from data science. To become qualified as an actuary, one Once you have finished your exams and attained an ASA or FSA accreditation, being an actuary is viewed as an easier, less stressful work life with regular hours. Get your hands dirty, code! Here are their main reasons for the choices they made between being an actuary or data scientist. Explain like I'm a college freshman: When actuaries say how likely is the CAS to be absorbed by the SOA? document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Send us your resume, and we'll find the jobs that are the best match for you. And I'd love to know if data scientists out there have other job boards/portals that they use on a regular basis. Explore, clean and graph the data. @keyframes ibDwUVR1CAykturOgqOS5{0%{transform:rotate(0deg)}to{transform:rotate(1turn)}}._3LwT7hgGcSjmJ7ng7drAuq{--sizePx:0;font-size:4px;position:relative;text-indent:-9999em;border-radius:50%;border:4px solid var(--newCommunityTheme-bodyTextAlpha20);border-left-color:var(--newCommunityTheme-body);transform:translateZ(0);animation:ibDwUVR1CAykturOgqOS5 1.1s linear infinite}._3LwT7hgGcSjmJ7ng7drAuq,._3LwT7hgGcSjmJ7ng7drAuq:after{width:var(--sizePx);height:var(--sizePx)}._3LwT7hgGcSjmJ7ng7drAuq:after{border-radius:50%}._3LwT7hgGcSjmJ7ng7drAuq._2qr28EeyPvBWAsPKl-KuWN{margin:0 auto} The IFoA membership report (http://goo.gl/qYgQ5Z) as of December 2014 shows 26,762 members, though only 56% of them are UK based. .ehsOqYO6dxn_Pf9Dzwu37{margin-top:0;overflow:visible}._2pFdCpgBihIaYh9DSMWBIu{height:24px}._2pFdCpgBihIaYh9DSMWBIu.uMPgOFYlCc5uvpa2Lbteu{border-radius:2px}._2pFdCpgBihIaYh9DSMWBIu.uMPgOFYlCc5uvpa2Lbteu:focus,._2pFdCpgBihIaYh9DSMWBIu.uMPgOFYlCc5uvpa2Lbteu:hover{background-color:var(--newRedditTheme-navIconFaded10);outline:none}._38GxRFSqSC-Z2VLi5Xzkjy{color:var(--newCommunityTheme-actionIcon)}._2DO72U0b_6CUw3msKGrnnT{border-top:none;color:var(--newCommunityTheme-metaText);cursor:pointer;padding:8px 16px 8px 8px;text-transform:none}._2DO72U0b_6CUw3msKGrnnT:hover{background-color:#0079d3;border:none;color:var(--newCommunityTheme-body);fill:var(--newCommunityTheme-body)} AHP accepts no liability for the content of this article, or for the consequences of any actions taken on the basis of the information provided unless that information is subsequently confirmed in writing. As a reminder, the majority of actuaries in the UK are members of the IFoA, and the majority of actuaries in India are members of the Institute of Actuaries India ("IoAI") whilst actuaries in the USA are either members of the Society of Actuaries ("SOA") if they are life and pensions actuaries, and the Casualty Actuarial Society ("CAS") if they are general insurance actuaries. Pure math and calculus just don't seem to show up in any well-paying jobs as Totally agree with many points mentioned above. The first step to success as a data scientist is to develop your current abilities in any form of data science sector you desire. The two professions together, along with clinicians, are well situated to tackle some of health cares most complex problems. They must know the nuts and bolts of mathematics, statistics, and financial theory. Wikipedia describes data science as the following: Data science is a concept to unifystatistics,data analysisand their related methods in order to understand and analyze actual phenomena with data. Given their statistics and Math background, actuaries have an edge over their data science counterparts, especially when it comes to making the pivot to data scientist role. For example, an actuary may have the vision to create a complex model using health care claims data, a data scientist may provide expertise on how to manipulate the data to fit that model, and the actuary may be aware of nuances in the data to account for while manipulating the data. Feel like you are missing out? First is technical and specifically relates to excessive dependency on SAS whilst data science increasingly relies on Python esp machine learning and deep learning. Great article! Can You Become a Web Developer Without a CS Degree? As data science (particularly ML) evolves, will human actuaries keep pace and shape up? There was a time where actuarial science was only about working in banks and companies with a finance background. Now that we have a description of what data science is, how does one qualify for a job as a data scientist? It is also one of the important aspects businesses need to consider in order to avoid risks. I would greatly welcome any comments, especially from data scientists, and even more so if someone can suggest a more accurate estimation for either the supply of talent or the demand (jobs). If you are an actuary, the competition seems less fierce with 9 potential applicants per job in the US, 19 in the UK and 55 in India. This may change as data science groups increase in size and management becomes more important. High growth job market where your skills are in demand, but matching what you want to do with how the company defines their opening is sometimes challenging. I have very little programming experience (not my cup of tea) but I enjoyed my statistics courses so far. It is easier for an actuary to learn data science than a data scientist to learn the insurance industry.. Advanced degree preferred (Masters or PhD.). To view or add a comment, sign in. Process, clean, and verify the integrity of data used for analyses. Lets End the Debate Actuary vs Data Scientist How did you guys price the body part for celebs? A nice point of view is given at In 2013, there were 5131 CAS members in the US, rising to 5,503 in 2014, a growth rate of 7.66%. We used Analysis Services, and therefore Microsoft decision trees as classifiers, which builds statistical models based on Bayesian networks instead of the traditional Entropy, or the Gini index methods. Create an account to follow your favorite communities and start taking part in conversations. WebAnswer (1 of 3): I work for a healthcare insurance plan. Become a Data Scientist The actuarial profession is much more established and has traditionally been more narrowly focused on pricing coverages and estimating reserves within the insurance industry. ._1QwShihKKlyRXyQSlqYaWW{height:16px;width:16px;vertical-align:bottom}._2X6EB3ZhEeXCh1eIVA64XM{margin-left:3px}._1jNPl3YUk6zbpLWdjaJT1r{font-size:12px;font-weight:500;line-height:16px;border-radius:2px;display:inline-block;margin-right:5px;overflow:hidden;text-overflow:ellipsis;vertical-align:text-bottom;white-space:pre;word-break:normal;padding:0 4px}._1jNPl3YUk6zbpLWdjaJT1r._39BEcWjOlYi1QGcJil6-yl{padding:0}._2hSecp_zkPm_s5ddV2htoj{font-size:12px;font-weight:500;line-height:16px;border-radius:2px;display:inline-block;margin-right:5px;overflow:hidden;text-overflow:ellipsis;vertical-align:text-bottom;white-space:pre;word-break:normal;margin-left:0;padding:0 4px}._2hSecp_zkPm_s5ddV2htoj._39BEcWjOlYi1QGcJil6-yl{padding:0}._1wzhGvvafQFOWAyA157okr{font-size:12px;font-weight:500;line-height:16px;border-radius:2px;margin-right:5px;overflow:hidden;text-overflow:ellipsis;vertical-align:text-bottom;white-space:pre;word-break:normal;box-sizing:border-box;line-height:14px;padding:0 4px}._3BPVpMSn5b1vb1yTQuqCRH,._1wzhGvvafQFOWAyA157okr{display:inline-block;height:16px}._3BPVpMSn5b1vb1yTQuqCRH{background-color:var(--newRedditTheme-body);border-radius:50%;margin-left:5px;text-align:center;width:16px}._2cvySYWkqJfynvXFOpNc5L{height:10px;width:10px}.aJrgrewN9C8x1Fusdx4hh{padding:2px 8px}._1wj6zoMi6hRP5YhJ8nXWXE{font-size:14px;padding:7px 12px}._2VqfzH0dZ9dIl3XWNxs42y{border-radius:20px}._2VqfzH0dZ9dIl3XWNxs42y:hover{opacity:.85}._2VqfzH0dZ9dIl3XWNxs42y:active{transform:scale(.95)} Contact the SOA and learn about their Predictive Analytics Initiative: Continue working in the insurance industry while you are accomplishing all of this. Data is everywhere these days and it's impossible to escape the constant stream of "data science" blog and articles - not just on Linkedin but the internet in general. ._9ZuQyDXhFth1qKJF4KNm8{padding:12px 12px 40px}._2iNJX36LR2tMHx_unzEkVM,._1JmnMJclrTwTPpAip5U_Hm{font-size:16px;font-weight:500;line-height:20px;color:var(--newCommunityTheme-bodyText);margin-bottom:40px;padding-top:4px;text-align:left;margin-right:28px}._2iNJX36LR2tMHx_unzEkVM{-ms-flex-align:center;align-items:center;display:-ms-flexbox;display:flex}._2iNJX36LR2tMHx_unzEkVM ._24r4TaTKqNLBGA3VgswFrN{margin-left:6px}._306gA2lxjCHX44ssikUp3O{margin-bottom:32px}._1Omf6afKRpv3RKNCWjIyJ4{font-size:18px;font-weight:500;line-height:22px;border-bottom:2px solid var(--newCommunityTheme-line);color:var(--newCommunityTheme-bodyText);margin-bottom:8px;padding-bottom:8px}._2Ss7VGMX-UPKt9NhFRtgTz{margin-bottom:24px}._3vWu4F9B4X4Yc-Gm86-FMP{border-bottom:1px solid var(--newCommunityTheme-line);margin-bottom:8px;padding-bottom:2px}._3vWu4F9B4X4Yc-Gm86-FMP:last-of-type{border-bottom-width:0}._2qAEe8HGjtHsuKsHqNCa9u{font-size:14px;font-weight:500;line-height:18px;color:var(--newCommunityTheme-bodyText);padding-bottom:8px;padding-top:8px}.c5RWd-O3CYE-XSLdTyjtI{padding:8px 0}._3whORKuQps-WQpSceAyHuF{font-size:12px;font-weight:400;line-height:16px;color:var(--newCommunityTheme-actionIcon);margin-bottom:8px}._1Qk-ka6_CJz1fU3OUfeznu{margin-bottom:8px}._3ds8Wk2l32hr3hLddQshhG{font-weight:500}._1h0r6vtgOzgWtu-GNBO6Yb,._3ds8Wk2l32hr3hLddQshhG{font-size:12px;line-height:16px;color:var(--newCommunityTheme-actionIcon)}._1h0r6vtgOzgWtu-GNBO6Yb{font-weight:400}.horIoLCod23xkzt7MmTpC{font-size:12px;font-weight:400;line-height:16px;color:#ea0027}._33Iw1wpNZ-uhC05tWsB9xi{margin-top:24px}._2M7LQbQxH40ingJ9h9RslL{font-size:12px;font-weight:400;line-height:16px;color:var(--newCommunityTheme-actionIcon);margin-bottom:8px} Instead, data scientist job openings often prefer, or even require, that the candidate has an advanced degree in a relevant field of study (e.g., a PhD. Talking about tools, experience with tools such as SAS, Apache Spark, MATLAB, Microsoft Excel, ggplot2, Tableau, Jupyter etc. Data science is still a very young profession, though, so its reputation will certainly evolve over time. Like literally! However, that doesnt mean the way of work would also be the same. Conference, in-person (Bangalore)Machine Learning Developers Summit (MLDS) 202319-20th Jan, 2023, Conference, in-person (Bangalore)Rising 2023 | Women in Tech Conference16-17th Mar, 2023, Conference, in-person (Bangalore)Data Engineering Summit (DES) 202327-28th Apr, 2023, Conference, in-person (Bangalore)MachineCon 202323rd Jun, 2023, Conference, in-person (Bangalore)Cypher 202320-22nd Sep, 2023. Data Science is a new profession that grew in response to an abundance of data all around us, the availability of sophisticated analytical tools, and companies within various industries viewing their data as a potential gold mine for valuable insights. Like the idea of taking exams that the company will pay for, even pay you for the time you spend studying, versus paying for your own Masters degree. @keyframes _1tIZttmhLdrIGrB-6VvZcT{0%{opacity:0}to{opacity:1}}._3uK2I0hi3JFTKnMUFHD2Pd,.HQ2VJViRjokXpRbJzPvvc{--infoTextTooltip-overflow-left:0px;font-size:12px;font-weight:500;line-height:16px;padding:3px 9px;position:absolute;border-radius:4px;margin-top:-6px;background:#000;color:#fff;animation:_1tIZttmhLdrIGrB-6VvZcT .5s step-end;z-index:100;white-space:pre-wrap}._3uK2I0hi3JFTKnMUFHD2Pd:after,.HQ2VJViRjokXpRbJzPvvc:after{content:"";position:absolute;top:100%;left:calc(50% - 4px - var(--infoTextTooltip-overflow-left));width:0;height:0;border-top:3px solid #000;border-left:4px solid transparent;border-right:4px solid transparent}._3uK2I0hi3JFTKnMUFHD2Pd{margin-top:6px}._3uK2I0hi3JFTKnMUFHD2Pd:after{border-bottom:3px solid #000;border-top:none;bottom:100%;top:auto} Agility trial: How can agile methodology benefit insurance? If you don't like programming you might not like being a data scientist - a lot of it involves coding models in R or other languages. When he is not writing or making videos, you can find him reading books/blogs or watching videos that motivate him or teaches him new things. an actuary Working on a Big Data project would definitely give you a heads-up. can do is to pick up data science skills and stay competitive in the industry. I'm sure both supply and demand of data scientists kept increasing, but the real question is which one has outpaced the other. Press question mark to learn the rest of the keyboard shortcuts, https://ar.casact.org/a-view-from-silicon-valley-how-to-make-good-on-the-insurtech-promise/. TIL that in Singapore sales for "discipline" canes Infographic: 49ers v Panthers Divisional Preview, Intro to Bayes theorem for intermediate developers, Correlation vs. Causation (x-post from /r/geek). They already have good background in probability & statistics and maybe some programming skills as well. Even though you are working as an actuary, try working on a big data project or be a part of a team that is working on one. Your email address will not be published. In data science, programming and the right set of tools play a major role. If you say programming is not your strong suit, I would go the actuarial route. How To Become An Actuary Forbes Advisor For now, each individual data scientist must build their own reputation as domain experts in their field of practice. The actuarial accreditation societies are trying to add this expertise to actuaries through the exam process. The actuarial exams are tailored towards insurance mathematics and insurance domain knowledge. WebAnswer (1 of 2): At the upper end, actuaries can make solid 6 figures $250,000 is not unheard of. Linkedin, for better or for worse, has become ubiquitous within the professional, knowledge intensive economy. Even on Quora, this is a burning topic. Great benefits. ._1LHxa-yaHJwrPK8kuyv_Y4{width:100%}._1LHxa-yaHJwrPK8kuyv_Y4:hover ._31L3r0EWsU0weoMZvEJcUA{display:none}._1LHxa-yaHJwrPK8kuyv_Y4 ._31L3r0EWsU0weoMZvEJcUA,._1LHxa-yaHJwrPK8kuyv_Y4:hover ._11Zy7Yp4S1ZArNqhUQ0jZW{display:block}._1LHxa-yaHJwrPK8kuyv_Y4 ._11Zy7Yp4S1ZArNqhUQ0jZW{display:none} ._3oeM4kc-2-4z-A0RTQLg0I{display:-ms-flexbox;display:flex;-ms-flex-pack:justify;justify-content:space-between} Data science and actuary science are two distinct fields, however they do share many similarities. Data scientists and actuaries both have similar skill sets, responsibilities and educational requirements, which they both use to analyze massive amounts of data. The career outlook for both jobs is good and many people think the two fields will ._12xlue8dQ1odPw1J81FIGQ{display:inline-block;vertical-align:middle} It would be great if you do a project that is beneficial for the organisation as well, as you would be able to understand the nuts and bolts in real-time. Data Science The first step to becoming an actuary is completing a business administration bachelors or an undergraduate degree in actuarial science, Many firms have analytics groups made up of actuaries and non-actuaries with predictive modeling skills. In more recent years, topics in predictive analytics and the use of statistical software such as SAS and R to analyze health care data has been added to the exam syllabus. Press J to jump to the feed. It is only through understanding your long-term career path that we can confidently find you the right fit with the right employer. .FIYolDqalszTnjjNfThfT{max-width:256px;white-space:normal;text-align:center} WebActuaries are more expensive than data scientists, and (generally) less adept at building models and using the data. How To Become An Actuary: Responsibilities, Practice Areas And I am in fact confused about what I wanna do. WebHi there, I am torn between choosing statistician/data scientist or actuary career. Again, I will simplify things somewhat by ignoring the small number of actuaries can be members of more than one profession or the very small number of US general insurance actuaries doing SOA exams. Data Scientists looking for a more structured environment can transition to being an actuary through the actuarial societys accreditation process. More generally, actuaries apply rigorous mathematics to model matters of uncertainty. How is the work different? I want to major in math, and I don't mind statistics or coding (I just don't want them to be the main focus of my degree). I couldn't help but think: are we asking the right question here? To become an actuary, you must first complete the requisite education and find an entry This is a relatively new question that many are pondering: Data Scientist or Actuary? Now, that does not mean that data scientists can seamlessly cross industries. There are only a few pathways out of majoring in math and I obviously want to take the higher pay option, which is actuarial science. To become qualified as a health actuary, one must pass a series of rigorous exams that requires mastery in mathematical and statistical insurance concepts as well a deep level of health insurance domain knowledge. Dont have any? To become qualified as an actuary, one must pass the actuarial exams which typically take between 5 to 10 years to finish (many do not finish). Gain some Hadoop experience. How can I become an actuary and a data scientist at the I think a big reason for this was that they were not afraid to embrace new technology, such as advanced analytics and data science to grow the business. Data Science vs Actuary: Which Required fields are marked *. Some extremely technical bachelors can succeed but they tend to be funneled into data analyst jobs versus data scientist. I was also really surprised by how much the "counter" for "data scientists" increased on Kaggle between my first draft (end of August) and my publication (end of September). There are two basic challenges that actuaries face inorphing into data scientists. Like the idea of working in insurance a stable, lucrative industry. Im finishing my masters in stats to get a job in data science. After reviewing a couple of dozen job openings on Glassdoor, we came up with the following generic description of job duties and qualifications for data scientists: An actuary is a professional that is trained in the discipline of actuarial science. All else being equal, I would far rather be part of a smaller candidate pool; that should hopefully give me higher visibility. 12 jobs for actuaries. The actuarial profession has been around a long time and is well established in the United States. It is this curiosity that best defines a data scientist. Data science and actuary science are two distinct fields, however they do share many similarities. Data Science has become the trendiest career path and because of that, we see several sub-domain emerging and even the skills required to become a data science professionals are significantly more. Data is something that both actuaries and data scientists crave the more data these professionals get, the better they do well at their jobs. Should I become a data scientist or an actuary? - Quora As recruiters in the actuarial and data science area we see actuaries including data science in their job most effectively when they are part of a larger organization that has both an actuarial group and a data science group. Assuming money isn't an issue, I would choose grad school were I you. Pursue a degree in data science and obtain all required forms of certifications. Plus you have to pay actuaries to study, which makes the expense issue worse. I'm going to ignore the fact that a small number of actuaries can also double up as data scientists as it does not meaningfully impact on the numbers. That said, it rapidly became apparent that the salary issue was going to be very difficult to unpick - once you find a reliable salary survey, you have to adjust for exchange rate, income tax (which varies from state to state in the US), cost of living etc. Additionally, people who are interested in becoming data scientists can enroll in online boot camps or courses to WebData scientists can arrive at the field from a variety of backgrounds, such as math, computer science, and business. Hybrid roles combining strength from each roles also collaborating to complement each other can be a great start to see where it can go!! A large portion (but certainly not all) of actuaries would consider themselves to be experts with Excel spreadsheets. Kaggle, on the other hand, markets itself as "Home of Data Science" and to me it feels like the "go-to" place for "data scientists", with a number of competitions (some with very high value prizes) for people to showcase their skills. WebHi All, I am contemplating whether I should pursue actuarial exams for actuarial science or grad school for data science/ statistics work. Main difference is data scientists are more aware about programming .Like the accreditation and credentials that are set up for the career path. So, as a rough estimate, 44% of Kagglers will be in the US, 6% in the UK and 10% in India, meaning we now have the following (very rough) estimate of the total actuarial and data science candidate pools. Even on Quora, this is a burning topic. In other words: if you are a "data scientist" in the USA, for every job advertised there are 54 potential total applicants, 59 in the UK and 70 in India. In contrast, data science can be applied to any industry that produces a large enough volume of data to build models and produce reliable predictions. Of course, just because there X number of total professionals working in a field doesn't mean they will ALL be looking for a job; but if we take the old recruitment "rule of thumb" that 10% of the professionals in any field are openly looking at any one time, the disparity between the two will be just as high (5.4 potential applicants per job in the US data science field compared with just 0.9 actuaries). .c_dVyWK3BXRxSN3ULLJ_t{border-radius:4px 4px 0 0;height:34px;left:0;position:absolute;right:0;top:0}._1OQL3FCA9BfgI57ghHHgV3{-ms-flex-align:center;align-items:center;display:-ms-flexbox;display:flex;-ms-flex-pack:start;justify-content:flex-start;margin-top:32px}._1OQL3FCA9BfgI57ghHHgV3 ._33jgwegeMTJ-FJaaHMeOjV{border-radius:9001px;height:32px;width:32px}._1OQL3FCA9BfgI57ghHHgV3 ._1wQQNkVR4qNpQCzA19X4B6{height:16px;margin-left:8px;width:200px}._39IvqNe6cqNVXcMFxFWFxx{display:-ms-flexbox;display:flex;margin:12px 0}._39IvqNe6cqNVXcMFxFWFxx ._29TSdL_ZMpyzfQ_bfdcBSc{-ms-flex:1;flex:1}._39IvqNe6cqNVXcMFxFWFxx .JEV9fXVlt_7DgH-zLepBH{height:18px;width:50px}._39IvqNe6cqNVXcMFxFWFxx ._3YCOmnWpGeRBW_Psd5WMPR{height:12px;margin-top:4px;width:60px}._2iO5zt81CSiYhWRF9WylyN{height:18px;margin-bottom:4px}._2iO5zt81CSiYhWRF9WylyN._2E9u5XvlGwlpnzki78vasG{width:230px}._2iO5zt81CSiYhWRF9WylyN.fDElwzn43eJToKzSCkejE{width:100%}._2iO5zt81CSiYhWRF9WylyN._2kNB7LAYYqYdyS85f8pqfi{width:250px}._2iO5zt81CSiYhWRF9WylyN._1XmngqAPKZO_1lDBwcQrR7{width:120px}._3XbVvl-zJDbcDeEdSgxV4_{border-radius:4px;height:32px;margin-top:16px;width:100%}._2hgXdc8jVQaXYAXvnqEyED{animation:_3XkHjK4wMgxtjzC1TvoXrb 1.5s ease infinite;background:linear-gradient(90deg,var(--newCommunityTheme-field),var(--newCommunityTheme-inactive),var(--newCommunityTheme-field));background-size:200%}._1KWSZXqSM_BLhBzkPyJFGR{background-color:var(--newCommunityTheme-widgetColors-sidebarWidgetBackgroundColor);border-radius:4px;padding:12px;position:relative;width:auto} Actuaries to study, which makes the expense issue worse I would go the science. 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