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Utilize advanced analytics and data science techniques, such as statistical analysis, machine learning, and predictive modeling, to analyze marketing data and predict future trends. Advanced knowledge of statistical analysis, machine learning algorithms, and predictive modeling techniques.
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Business Intelligence (BI), Database Design, Data Engineering, Data Modeling, Data Science, Data Visualization, Machine Learning, Project Management, Software Development, Stakeholder Relationship Management.
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Senior Manager-level candidates must have project management experience in cross-functional engagements including accounting/finance, FP&A, modeling, and/or tech implementation. Must be able to effectively communicate complex, technical topics to a novice, non-technical audience with a passion for collaboration, learning new technologies, and client success.
Full-timeExpandApply NowActive JobUpdated 3 months ago - UpvoteDownvoteShare Job
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Continue to develop and help drive the strategy for the organization's data strategy including data architecture, Reporting, Insights, Analytics, Operations, Predictive modeling, attribution and Machine Learning/AI.
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Experienced in using multiple data science methodologies to solve complex business problems (e.g. statistical analysis, research science, machine learning and deep learning techniques, data modeling, regression modeling, financial analysis, demand modeling, etc.
$90,000 - $120,000 a yearFull-timeExpandApply NowActive JobUpdated 11 days ago - UpvoteDownvoteShare Job
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Strong knowledge of statistical analysis, predictive modeling, machine learning, and optimization techniques. Demonstrated passion for continuous learning and staying updated on the latest advancements in analytics and transportation industry trends.
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Special Requirements: Must have 4 years of experience with:Advanced Python programmingOne of the following analytical frameworks: Econometric modeling, Case-control matching, Experimental design, Causal inference (including AB testing), Observational studies, or relatedAdvanced SQL for data extraction, transformation, and manipulationMachine Learning methods: Supervised and unsupervised learning with applications in NLP, Recommender systems, or Measurement.
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Education, Learning & Development: We offer college tuition and education assistance programs; Udemy Learning courses; and ongoing learning and development opportunities. Menu Data Modeling & Tools Analyst.
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And/or TensorFlow/Py Torch 3 years of experience coding on models that implement natural language translation, image recognition, and sequence to sequence deep learning models. Preferred Qualifications: Experience implementing and designing attention-based or transformer-based modeling (e.g. State of the art NLP and Vision models or any other sequence to sequence-based DL models.
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The Senior Lead Data will be responsible for partnering with Technology, Machine Learning, Product Architecture, and other Capital One teams to support the development of the Finance Tech Data Strategy into products and services we create and consume.
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Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across dating, personalization, growth, discovery, ads and search. Be an expert in matching algorithm and recommender systems machine learning models and own the implementation of dating recommendations in Grindr.
$160,000 - $220,000 a yearFull-timeExpandApply NowActive JobUpdated 8 days ago - UpvoteDownvoteShare Job
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The Reporting and Analytics team drives data-driven decision making at UCLA by providing delivering advanced data analytics, learning analytics, data modeling, automation, reporting and dashboarding, and other business intelligence solutions.
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The Financial Crimes Data Science Manager is primarily responsible for leading the development and validation of predictive and machine-learning models for specific business needs using statistics, advanced mathematical techniques, and/or computer science.
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We're seeing candidates who are excited at the prospect of unlocking the secrets held by a data set, and you're fascinated by the possibilities presented by advanced data modeling, machine learning, and artificial intelligence.
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Modeling, machine learning, mixed-models) Ongoing projects include: i) using real-time fMRI neurofeedback for endogenous regulation of dopaminergic midbrain activity, ii) understanding how midbrain neuromodulation contributes to downstream behavioral and cognitive outcomes (e.g. memory and decision-making), iii) identifying how different motivational states influence learning and memory formation.
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Being punctual at work may not be something you’ve given much thought to, but it’s the foundation for building a successful career. All of your technical or job-specific skills will be in vain if your peers and superiors can’t trust you to show up on time and do the work. In fact, Simon Sinek once famously said that
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The summer had economists from around the globe embroiled in a debate about a possible recession coming in the next few years (or months). As of October 2022, the U.S. Labor Department data put the current inflation rate at 7.7%. The recent layoffs in the tech industry are just the first of what is soon to be a string of cutbacks by companies looking to save costs. For recruiters, this means freezes in hiring and fewer openings. It will also include the uphill task of finding the best candidates for them from the coming influx of recently laid-off job seekers. Now is probably a good time to brace for tough times in the next few years in the talent acquisition industry. To survive and thrive recruiting in a recession, here are some hard truths you will need to accept.
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Collaborative Recruiting: The Key to a Better Talent Acquisition Strategy
Talent acquisition is a multi-stage process where candidates undergo various application steps before getting hired. The unfortunate reality is that it is a labor-intense system, with the hiring manager and recruiter often handling all of the work on their own. Ask any one of them, and you will hear about the overabundance of applications and the demanding task of filtering through them to find the best candidates. The quality of talent suffers under the weight of all that work on one person's hands. It's not easy, but as many companies are starting to realize, there is a better way. The future of talent acquisition lies in collaborative recruiting!