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What we are searching for Master’s or PhD degree in quantitative discipline: Computer Science, Engineering, Data Science, Math, Statistics or related fields4+ years of industry experience in applying data science and modeling methodologies: regression model, survival model, ensemble modeling, NLP, recommendation algorithm, clustering, deep learning algorithm, experimental design (Multivariate/B testing) and nonparametric Bayesian modeling etc.
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The ideal candidate will possess deep expertise in data science, machine learning, AI, and strategic business acumen. Qualifications:Minimum of 10 years of experience in data science or advanced analytics, with at least 5 years in a leadership role.
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1 year experience with Smartsheet Datamesh Understanding of data-warehousing and data-modeling techniques Knowledge of industry-wide visualization and analytics tools including Tableau Good interpersonal skills and positive attitude Ability to develop effective working relationships.
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Incumbents whose focus is primarily on analysis and modeling of financial, marketing or pricing data should be matched to Finance, Market Research or Pricing as appropriate. Incumbents whose focus is primarily on experimental design and advanced or complex statistical analysis and modeling of datasets should be matched to Statistician/Mathematician.
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Expertise is preferred in public health surveillance, applied population health science, climate change adaptation, mathematical modeling and systems research, food safety economics, and/or social and environmental factors that may affect the epidemiology of exposure to foodborne diseases and antimicrobial resistance.
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We are seeking a highly motivated, experienced, and talented Machine Learning Engineer to join our AI research team and contribute to our innovative projects in the field of Large Language Modeling (LLM) and clinical data analysis.
$152,000 - $205,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Master's degree in a discipline directly related to data analytics (e.g. statistics, computer science, data science, data modeling, predictive analytics) from an accredited university plus 3+ years of industry experience in Analytics/Data Science.
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Along with proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch) and experience or familiarity with data science platforms such as Databricks, H2O, AWS Sagemaker. They can encompass both quantitative and methodical skills or more creative and innovative skills like establishing cause -and-effect in a high-tech business environmentEstablish and operate a Data Science and AI Community of Practice (CoP) for the purposes of sharing standard methodologies and frameworks, discussing emerging trends and technologies, and cross-team sharing of ideas and initiatives.
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Experience with industry standard data cataloging, data quality, and/or data modeling technologies. Train other teams on data tools and provide guidance on data modeling and practices.
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Experience with data modeling, machine learning algorithms, optimization, and data science techniques e.g. decision trees, SVM, neural networks, time series, forecasting, classification, regression, clustering, cross-validation, and model evaluation.
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Experience working with data engineering, data warehousing, business analytics, data science or data modeling teams is highly desirable. Minimum of 5 years of experience as a Scrum Master leading Agile teams, preferably in data management or integration projects.
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Ideal candidates will have some background in stochastic processes, knowledge of statistical modeling functions and implications thereof, and coding experience to write queries, transform data, calibrate, validate and debug models and build automation workflows.
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Basic Qualifications (Required Skills/Experience):Bachelor of Science degree from an accredited course of study in engineering, engineering technology (includes manufacturing engineering technology), chemistry, physics, mathematics, data science, or computer science.
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Experience with predictive modeling in SAS, Python, R or other. Experience working in SQL, Alteryx or other data management and Business Intelligence tools. Bachelor's Degree preferably in Computer Science, Information Technology, Computer Engineering, Finance, Economics, Math or related IT discipline; or equivalent experience.
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Strong experience in Python, R, SQL, and other relevant tools for data analysis and statistical modeling. PhD/MS degree in Computer Science, Statistics, Applied Mathematics, or related fields.
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modeling data science jobs
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With the advent of desktop computers, the arduous task of scouring through weekly job classifieds became a thing of the past. The mid-1990s brought about a new era where job seekers could easily search and apply for jobs online. The introduction of AOL's Instant Messaging feature provided an even faster means for employers and candidates to communicate and schedule interviews. As smartphones became more pervasive in the early 2000s, hiring managers increasingly used phone calls for screening and interviewing candidates. Despite this trend, over 80% of interviews still took place in person.
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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.