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As a Quantitative UX Researcher, you will be responsible for Survey Management, which includes design, implementation and deployment to collect generative and evaluative quantitative data, ensuring customer questions are clear, unbiased, and aligned with experience metrics, survey design format, scales, standards and research objectives.
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2-3 years of relevant experience post Advanced degree (MS, PHD) in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, and Econometrics.
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Bachelors’ or above in quantitative discipline: Statistics, Applied Mathematics, Economics, Computer Science, Engineering, or related field. We use data, research, strategy, and empathy to guide multidisciplinary teams toward a common goal, balancing diverse perspectives and empowering our teams to do great work.
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Required Qualifications: Degree in Applied Math, Physics, Engineering, Quantitative Finance, Computer Science or other quantitative disciplinesUp to 4 years of experience in a relevant role such as software or quant developmentProficiency in at least one of Python, C.
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Bachelor's degree in a quantitative field like Data Science, Statistics, Applied Mathematics or Econometrics required. Support academic success for our students through the applied use of advanced analytics, and help to identify, pilot and test initiatives for prospects, students, and faculty that drive improved academic outcomes and achievements.
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Bachelor, Master's, or PhD degree in Computer Science, Engineering, Applied Mathematics, Statistics or related STEM field. 2-4 years of experience working in a quantitative research capacity with a focus on Ags, Energy, Metals, Fixed Income, FX, or Equity Index strategies.
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Requirements: Requires a Master’s in Computer Science, Data Science, or a related field plus two (2) years of experience as an Applied Scientist, Data Scientist, or any occupation/position/job title involving experience in a highly quantitative field.
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A BA/BS degree in a quantitative discipline: computer science, statistics, applied mathematics, operations research, engineering, economics, etc. 2+ years of experience with Tableau, Looker or comparable data visualization tools.
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Bachelor's degree in Computer Science, Statistics, Data Science, Applied Mathematics, or related quantitative disciplines (Master's degree preferred). · Deep understanding of and experience with ML models and ML engineering, advanced statistics, time series forecasting, best practice application of data science in a business context (e.g., back-testing & piloting), model architecture, and use cases.
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Academic training in a quantitative field such as Computer Science, Data Science, Mathematics, Statistics, Bioscience, or Engineering. The ideal candidate will have a background in applied statistics, economics or a related quantitative field, will have strong business acumen, experience working with large data sets, and experience interacting with business partners.
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Common areas of study include but are not limited to:Economics Computer Science Data Science Engineering Financial, Stochastic, and other applied Math Operations Research Statistics & econometrics Other STEM majors.
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Required Skills:Bachelor's or Master's degree in Computer Science, Statistics, Applied Mathematics, Data Management, Information Systems, Information Science, or a related quantitative field, or equivalent work experience.
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Must have exp w/ data science skills, incl estimation methods, time series anlysis, stat inference methods, & mach learning techniques as applied in quantitative research. Must have Master's or equiv in CS, Fin'l Eng'g, Math, or rel field plus 3 yrs exp in bldg quantitative fin'l models, handling time-series data & lrg data sets or rel exp; OR PhD or equiv in CS, Fin'l Eng'g, Math, or rel field.
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Education: bachelor's degree in a technical field such as: Operations Research, Computer Science, Statistics, Applied Mathematics, Engineering or related quantitative / STEM majors.
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Experience using gridded data, observational data and satellite data to drive quantitative research in academia or industry. Expertise in relevant topics such as: sub-seasonal predictability, data assimilation and numerical weather prediction, tropical variability, synoptic meteorology, renewable energy forecasting.
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applied quantitative data science jobs in New York, NY
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