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As Data Scientist Lead - Risk Data and Analytics, you will be responsible for developing AI models using machine learning, deep learning, and natural language processing, particularly transformer models and Generative AI. Your expertise will be instrumental in developing and implementing predictive modeling solutions for the risk and compliance organization, enabling them to proactively identify and mitigate risks.
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The Data Science team is hiring an experienced Machine Learning Engineer with a background building machine learning and statistical modeling frameworks from scratch.
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Masters degree in Computer Science, Applied Mathematics, Data Science, Computational Physics/Chemistry or related technical subject area; PhD degree preferred. Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products.
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Scientific background in life sciences or engineering and demonstrated expertise in one or more of the following areas: data analytics, data visualization, statistics, machine learning, predictive modeling, decision analysis under uncertainty, artificial intelligence, Python and/or R, app development, and Tableau dashboard development.
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8 years of experience in a predictive analytics or data analysis OR Advanced Degree (e.g., Master’s, PhD) in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative field and 6 years of experience in predictive analytics or data analysis.
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We combine best of breed engineering practices, subject matter expertise, design services, computer science, and data analytics with innovative approaches in artificial intelligence and machine learning, AR/VR, R&D, and physics-based modeling and simulation providing tailored solutions addressing our customer's unique threat requirements.
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An educational background in data science, machine learning, computer science, statistics/ biostatistics, mathematics, computational biology/chemistry, chemical/biomedical engineering, pharmaceutical sciences, or pharmacometrics.
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Typical role includes working on a wide range of activities such as working with complex structured and unstructured datasets, developing/recommending novel machine learning tools, data visualizations, automation of analytics workflows, disease progression models, mechanistic and empirical PK/PD models, clinical trial simulations, literature meta-analysis using quantitative approaches and statistical modeling of historical and preclinical data.
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Experience in core data science and predictive analytics methods: Statistics (t-tests, Poisson process), Segmentation and clustering techniques, predictive modeling: e.g., regression, classification, Time Series analysis: e.g., ARIMA, Traditional machine learning methods: e.g., Random Forest, ensemble model techniques, Optimization: e.g. linear programming.
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Serves as a mentor to data scientists in modeling, analytics, computer science, discernment, and other interpersonal skills. Establishes and maintains standard processes for engaging with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and model risk management standards.
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Key skills include data analysis techniques (e.g., A/B testing, association rule learning, cluster analysis, pattern recognition and predictive modeling), data analysis and visualization software (e.g., Domo, Looker, PowerBI, Qlik, Tableau, SiSense), object-oriented programming languages (Java, Python, R, Ruby, Scala, C.
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Experience performing data science / analytics, such as statistical modelling, predictive analysis, or machine learning using Python, SQL, PySpark and cloud-based tools such as Big Query, Databricks.
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You have 2-5 years of successful technical experience in the domain of predictive analytics (e.g. data science, machine learning, data mining, and statistics related work); preferably in the Oil & Gas industry.
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Lead team focused on new technologies such as process automation, complex predictive modeling, artificial intelligence, machine learning and computer vision to improve how we do business.
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The Enterprise Business Intelligence Data Scientist is responsible for leading predictive analytics, statistical modelling, machine learning and data mining effort for implementing solutions and analysis of clinical care utilization and performance across the JPS Health Network.
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machine learning predictive modeling analytics data science computer engineering jobs Company: Inc in Raleigh, West Virginia
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