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Net, C#, Qlik, Power BI, Machine Learning, Azure Data Factory, RedShift, UiPath, Cloud, RPA, AWS, Redshift, Kinesis, QuickSight, SageMaker, S3, Databricks, AWS Lake Formation, Snowflake, Python, Qlik, Athena, Data Pipeline, Glue, Star Schema, Data Modeling, Performance Tuning, SQLIndividual salaries that are offered to a candidate are determined after consideration of numerous factors including but not limited to the candidate s qualifications, experience, skills, and geography.
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Statistical and machine learning techniques for analyzing financial data, including survival analysis, time series analysis, GLM, logistic regression, XGboost, LightGBM, Random Forest, Neural Networks, decision tree analysis, and cluster analyses.
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We expect our Machine Learning Engineer to have excellent skills with Python and its machine learning / natural language processing libraries, and have working experience with both structured and unstructured data.
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Design, implement, and optimize data solutions using DataBricks for data analytics and machine learning applications. Knowledge of machine learning algorithms and techniques.
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This role will be part of a multidisciplinary actuarial/data science team designing and delivering powerful, analytical tools utilizing statistical modeling, machine learning, and advanced data engineering techniques to enhance and overhaul core actuarial processes.
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Preferred/Nice-to have Qualifications: Experience in a late-stage startup environmentExperience building and bringing to practical use, knowledge graphs and graph-based machine learning modelsExpertise with embeddings, multimodal fusionExperience working with sensitive healthcare data, medical imaging modalities, clinical workflows, and healthcare terminology.
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An entrepreneurial and meticulous Senior Data Engineering / Machine Learning Ops Manager who oversees the development of data for measurement and reporting across United’s digital media.
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Driven by data science and powered by machine learning, our offering analyzes officer performance data in order to identify potentially problematic behavior. Assist experienced data scientists in constructing, enhancing, and validating machine learning and statistical models relating to law enforcement risk and effectiveness measurement.
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Exposure to data science methodologies, especially GLMs, decision trees, and machine learning. 3+ years in an analytical capacity as a data analyst, actuarial analyst, or underwriting analyst.
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At least 3 years of experience implementing full lifecycle ML automation using MLOps(scalable development to deployment of complex data science workflows) At least 4 years of experience leading teams developing Machine Learning solutions At least 1 year with LLM based conversational AI systems Preferred Qualifications: Master’s degree or PhD in Engineering, Computer Science, a related technical field, or equivalent practical experience with a focus on modern AI techniques.
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We have openings for receptionist, data entry, customer service, collections, office managers, call center, administrative assistant, accounts payable clerk, accounts receivable clerk, file clerk, warehouse, assembly, production, pickers, packers, forklift, machine operators, and maintenance mechanics.
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Experience in running projects with MMM (Media mix Modeling) / econometric models preferred or related analytical models using multivariate regression or machine learning algorithms. Knowledge of and experience using key internal and external consumer data sources, such as Consumer panel data, first party (e.g. e-mails, MAIDs), the second party (e.g. Retailer Media, JBP (Meta, Amazon), Retailer scan sale data, technology generated data) and 3rd party (e.g. Kantar, Dynata, Nielsen, IRI, Fetch.
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Awareness of machine learning software frameworks including PyTorch, pgvector, JAX, Neuron, MXNet, TensorFlow, or similar technologies. Heroku is seeking a senior software engineer for our exciting new AI, machine learning, and LLM offerings for our customers.
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2+ years of industry experience in a computational area such as data science, computer science, machine learning, artificial intelligence, statistics, or graph algorithms. In this role, you will lead and contribute to new and ongoing research and development efforts that advance Epsilon's identity match abilities - leveraging data science, machine learning and other disciplines to create new data-driven capabilities for our real-time digital marketing business.
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Using natural language processing (NLP), machine learning (ML), Generative AI, and other relevant AI technologies and platforms; We make impactful decisions by mixing mind and machine to leverage data, understand and navigate risk, and help our clients gain a competitive edge.
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