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Develop experimental and analytic plans for machine learning algorithms and data modeling processes, use of strong baselines, and ability to accurately determine cause and effect relations.
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This role will be part of a dedicated hybrid actuarial/data science team designing and delivering powerful analytical tools utilizing statistical modeling, machine learning, cloud computing, and big data platforms to enhance or overhaul core actuarial processes.
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We seek instructors with professional experience in data science and analytics, who can teach in areas relevant to data science including data visualization, multivariate statistics, time series analysis, predictive modeling and machine learning.
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Machine learning or deep learning algorithms, natural language processing techniques. Apply statistical, machine learning, and NLP techniques to identify patterns, extract meaningful insights, or develop new tools or solution for the business stakeholders.
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You have experience with statistical modeling, hypothesis testing, machine learning, deep learning, optimization and other data science techniques to implement end-to-end data science projects.
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Proficiency with Machine Learning concepts and modeling techniques to solve problems such as clustering, classification, regression, anomaly detection, simulation and optimization problems on large scale data sets.
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We are at the forefront of Machine Learning solutions that influence tangible business outcomes. General ML engineering skills such as texting mining, and statistical analysis methods, such as classification, association rules, sentiment analysis, topic modeling, time-series analysis, statistical inference, and validation methods.
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6-9 years of experience with predictive modeling, databases, computational simulation, clustering, machine learning, and remote computing in academia or industry. Model and build Machine Learning, Statistical algorithms on top of our vast number of datasets.
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Knowledge of predictive modeling, statistical sampling,optimization, machine. learning and artificial intelligence techniques. Responsible for independently conducting quantitative analytics and modeling projects.
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Apply analytical techniques, including linear/non-linear models, clustering, optimization, forecasting, machine learning, and neural networks. · Experience developing machine learning algorithms.
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Experience in at least four of the following six areas: 1) data analysis and relational-style query languages; 2) machine learning and/or statistical modeling; 3) data visualization; 4) a high-level programming language; 5) distributed computing; 6) understanding of healthcare.
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Lead development of key technology and process transformation deliverables, including Generative AI, Predictive modeling, and machine based learning. , strong Excel skills; understanding of new technologies, such as Generative AI and Machine Learning.
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Integrating innovative approaches in individual and team work products such as machine learning, artificial intelligence, data mining, predictive modeling, neural networks and simulation to mitigate price/value leakage across the pursuit and delivery phases of an engagement.
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As Data Scientists as Stripe, it's our mission to ensure that the company strategy, products, and userinteractions make smart use of our rich data, usingtechniques like machine learning, statistical modeling, causal inference, optimization, experimentation, and all forms of analytics.
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As a Data Scientist, you will play a crucial role in our team by leveraging your expertise in statistics, machine learning, programming, and data modeling to derive valuable insights from complex and high-dimensional datasets.
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modeling machine learning jobs in Chicago, IL
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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.
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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.
Recruitment strategies that are weird, but actually work
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.