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We're looking for a top-notch search engineering leadership in the area of information retrieval, search indexing, Elasticsearch, Lucene, algorithms, relevance & ranking, data mining, machine learning, data analysis & metrics, query processing, multi-lingual search, and search UX. This is an opportunity to make a huge impact in a fast-paced, startup-like environment in great company.
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Focus on data systems / ML Ops. o Big data tooling (BigQuery/snowflake/redshift/athena/presto/trino, structures like parquet/iceburg etc.) o Database optimization (SQL / noSQL, postgres, mysql, mongo, cockroach, dynamodb, could include full search tools: elastic, algolia, or vector DBs etc.
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Our team is dedicated to identifying and interpreting genetic signal at the scale of thousands of phenotypes and millions of individuals, making the knowledge gleaned from genetics accessible to AbbVies R&D community, and using data science and machine learning to maximize the value genetics brings to AbbVie.
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We use the best machine learning techniques to simplify product discovery for animal lovers looking for their pet needs on Chewy.com. Our outstanding multi-disciplinary team of data scientists, data engineers, software/front-end engineers, and product managers work together to power personalized recommendations and search for pet parents.
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The Data Science and ML team member develop innovative data science models and machine learning algorithms to drive digital product experience and sales growth for SGWS. This role will be focused on developing and deploying personalization and recommendation, search, experimentation, and marketing AI solutions.
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Experience in data modelling using HANA Studio, ABAP Development Tools (ADT), Code Performance Rules and Guidelines for SAP HANA, ADBC, Native SQL, ABAP Core data Services, Database Procedures, Text Search, ALV on HANA, and HANA Live models consumption.
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TELUS International AI-Data Solutions partners with a diverse and vibrant community to help our customers enhance their AI and machine learning models. The job involves analyzing and providing feedback on text, web pages, images and other types of information for leading search engines, using an online tool.
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Develop and manage machine learning models in production environments on cloud platforms such as Azure, AWS, or GCP. As a Machine Learning Operations Engineer, you will play a pivotal role in rapidly developing AI-powered software applications, with a focus on internal business applications driven by large language models.
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This role will challenge you to utilize cutting-edge machine learning techniques in the domain of predictive modeling, natural language processing (NLP), deep learning, reinforcement learning, query understanding, vector search (kNN) and image recognition to deliver significant impact for the business.
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Use data science methods, including but not limited to Artificial Intelligence/Machine Learning (AI/ML) approaches, to perform data processing (e.g. filtering), feature recognition, alarm threshold determination, pattern matching, statistical trending, scoring of detection quality, and integration of other information into decision making.
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Deliver reusable and easy-to-use tooling to integrate with existing data and machine learning systemsRequirements: Strong understanding of machine learning principles, especially in the context of LLMs.5+ years of proficiency in Python, including machine learning packages like Jax/Tensorflow or PyTorch.
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The associates will support various project-related tasks, including, but not limited to: data inventory development, scientific study coordination, data search and acquisition, statistical analysis, data formatting and calibration, mathematical model formulation, pipeline development, and machine learning.
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Strong technical and consultative experience managing ESI through the EDRM including staging, processing, culling, email threading, clustering, communication analysis, machine learning, predictive modeling, etc.
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Experience with machine learning, algorithm analysis, and data clustering. Understands Machine Learning and is able to apply machine learning, multivariable calculus, and linear algebra techniques and approaches, including but not limited to, k-nearest neighbors, random forests, and ensemble methods.
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The team leverages sophisticated machine learning, data science and big data technologies to understand customers' intent, provide relevant product and services through the best possible experience.
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The story of our US-Ukraine connection started when Kevin met Dave. Days away from quitting his last "real" job as a seafood plant manager, Kevin reconnected with a long-lost friend from high school. Little did he know, this long-lost friend was dating his soon-to-be business partner. After meeting Dave through their mutual connection, it didn't take long for the two to start bouncing business ideas off each other. The flow of creativity soon grew into a business partnership that has withstood the test of time.
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