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Perform data exploration using a combination of statistical programming languages (including, but not limited R, Python, SQL, SAS) and deploy predictive analytics and machine learning techniques to improve risk prediction, improve reserve, trend and financial forecasting in a manner that is actuarially sound, and enable real-time results and operational efficiencies.
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Some basic familiarity with advanced statistical or analytical techniques like time-series/autoregression, causalmodeling, hierarchical or ensemble models, or machine learning algorithms.
$117,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Data Analysis: Conduct advanced data analysis to uncover insights, identify trends, and develop actionable recommendations, using statistical analysis techniques, machine learning algorithms, and data visualization tools.
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Proficiency in programming languages such as Python, R, or SQL, with experience in libraries/frameworks such as TensorFlow, PyTorch, Scikit-learn, Langchain, etc. You understand the data science lifecycle and are experienced in development and implementation of advanced statistical models and machine learning algorithms.
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Experience with machine learning, data mining, econometric modeling, and general statistical modeling. Strong programming skills in at least one language, such as Python, R, C.
$192,500 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Familiar with one or more machine learning or statistical modeling tools such as R, Matlab, and scikit-learn. Deep understanding of statistical modeling, machine learning, deep learning, or data mining concepts, and a track record of solving problems with these methods.
$219,100 a yearFull-timeExpandApply NowActive JobUpdated 3 days ago - UpvoteDownvoteShare Job
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Every day at Figure is a journey in continuous learning yet a daily focus on getting work done that makes a difference. Familiarity with big data manipulation and analysis tools (e.g., SQL, Spark, Hadoop) along with experience building and implementing models in a cloud-based environment (e.g., Azure, AWS, etc.
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Strong knowledge of statistical techniques and machine learning algorithms for predictive modeling, including regression, classification, clustering, and time series analysis. Proficiency in programming languages such as Python, R, or SAS, and experience with data manipulation and analysis libraries.
$190,000 a yearFull-timeExpandApply NowActive JobUpdated 2 months ago - UpvoteDownvoteShare Job
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Demonstrated fluency and competency with statistical programming with the R programming language or the Python programming language. PhD in a field relevant to biomedical research (bioinformatics, biomedical engineering), or computer science (computer science, machine learning, artificial intelligence) or similar.
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Proficiency in programming languages like Python, R, or similar, with experience in data manipulation libraries like pandas, NumPy, Scikit-learn, and TensorFlow/PyTorch. Design and implement advanced machine learning models (e.g., deep learning, statistical modeling) to predict aortic disease risk progression.
$93,060.24 a yearFull-timeExpandApply NowActive JobUpdated 3 days ago - UpvoteDownvoteShare Job
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Utilize data mining, statistical analysis, and machine learning techniques to extract insights from large datasets. Proficient in programming languages such as Python, R, and SQL.
Full-timeExpandApply NowActive JobUpdated 17 days ago - UpvoteDownvoteShare Job
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Understanding of statistical modeling, machine learning, or data mining concepts, and a record of solving problems with these methods. Experience with programming language such as Python, R.
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When Rage Applying Strikes: How to Identify Unserious Candidates
As the job market remains highly competitive, we have seen a surge in "rage applying." This is when candidates apply to multiple jobs, often without considering whether they are truly interested in the role. Rage applying goes hand-in-hand with quiet quitting. Often, employees want to entertain the thoughts and feelings of leaving their job, but they aren't necessarily serious about leaving yet. Meanwhile, other employees engaging in this trend are actually trying to find a better role. As a recruiter, it can be hard to identify who are the real applicants in a sea full of quiet quitters, but understanding rage applying and identifying red flags will certainly help.
How to Increase Job Ad Exposure
In today's competitive job market, writing quality job ads is critical for attracting top talent to your organization. While networking and candidate referrals are prime real estate for finding qualified candidates, nothing beats the tried-and-true method of writing an extraordinary job ad. But while writing a great job ad is the first step, what's more important is increasing visibility. You could have the most detailed, well-written ad on the internet, but if no one sees it, then you are wasting time (and potentially money!). Employers often believe that job boards are the root of the problem, but you can learn how to increase job ad exposure by tweaking a few steps of your recruitment process.
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The job market has shifted significantly in recent years. The accelerated adoption of technology has not only pushed many companies into remote working arrangements but also increased the availability of supporting tools and technologies (i.e., video conferencing and collaboration software).
Building a Candidate Pipeline Through Internships
Building a candidate pipeline through a great internship program for local college students and recent graduates at local universities is a great and cost-effective way to attract and retain top talent. By offering meaningful and impactful work experiences, regular feedback, coaching, and mentorship, you can create a positive internship experience that will make your organization a sought-after destination for future employees. This not only benefits the organization in the short-term but also in the long-term, as you'll have a pool of well-trained and experienced candidates who may be interested in full-time employment once they graduate. Furthermore, building relationships with local universities and college students can increase brand awareness and build a positive reputation for your organization in the local community.
Hiring Transparency
Transparency in hiring refers to the open and honest communication and information sharing that takes place between employers and job candidates. It encompasses all aspects of the hiring process, from posting job descriptions to providing feedback on performance during and after the interview process. In today's job market, hiring transparency has become increasingly important for both employers and candidates alike.
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In the current candidate-driven job market, recruiters are looking for unique ways to attract talent. Some have resorted to even (dare we say it?) recruitment strategies on the border of weird and wacky. What can we learn from the unusual recruitment tactics that are being used and actually getting results? Here’s a rundown of some unique recruitment strategies that actually work.
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If you’re like most of us, you’d love to be wealthier. Having more money would alleviate stress. It would make it easier to pay your bills and buy nicer things. Maybe it’d allow you to spend more time with your kids and go on more vacations. You’re not alone if you wish you could somehow earn a more significant income.