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You will collaborate with experimental scientists to design and interpret experiments that validate and refine machine learning-generated hypotheses about novel MOAs. You will contribute to and drive publications, present results at internal and external scientific conferences, and help make code and workflows open source.
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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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Apply mathematical (geometry, linear algebra, numerical methods, error analysis) and machine learning methods to prototype and develop algorithms for converting raw depth sensor data into point clouds, 2D and 3D tracking like SLAM, 3D point cloud reconstruction, registration, and classification as well as texture projection and completion.
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5+ years of experience in data analysis or data science, with 3+ years focusing on machine learning problems, ideally in a relevant space (KYC, sanctions detection, anti-fraud detection, treasury management, crypto/blockchain data science.
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Experience in the usage of machine learning/AI tools in life science area(s) and handling life science datasets is preferred. Adapt data science algorithms (supervised and unsupervised learning, decision trees, neural networks, AI based image processing and feature extraction, Bayesian learning, etc) for modeling clinical trial data to support drug development.
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Boston, MA, Brooklyn, NY, San Francisco, CA, Washington, D.C., Remote, London, England 11-10-2023 HealthTech Azure Elasticsearch Docker AWS Python Machine Learning. 10+ years of professional experience as a data scientist or machine learning engineer with proven track record of delivering functional product oriented ML solutions.
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Experience with image analysis, machine learning, and deep learning frameworks Pytorch, TensorFlow-Keras). The incumbent will apply image analysis and machine learning in collaboration with a group of computational and biological scientists to perform translational oncology digital pathology research for programs in clinical development.
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Work with the embedded Machine Learning Engineers on the team and ML platform services to deploy models to the production environment and monitor ongoing performanceUse Python ML stack, LLMs, Pytorch, Snowflake, Airflow based tools, data platform and cloud services (both GCP & AWS) to get the job doneQualificationsYou Have: 5+ years of Machine Learning modeling experience.
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Our machine learning systems monitor and surface suspicious activity (money laundering, illegal activity and terms of service violations) for agent review. Work with the embedded Machine Learning Engineers on the team and ML platform services to deploy models to the production environment and monitor ongoing performance.
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The Computational Biology group in the Clinical Biomarkers & Diagnostics (CBD) department at is seeking a highly motivated Sr data scientist to join our team and contribute to develop machine learning modeling and prediction pipelines using multi-modal biomarker data from clinical trials.
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Working alongside our generalist consultants, Bain's Advanced Analytics Group (AAG) helps clients across industries solve their biggest problems using our expertise in data science, customer insights, statistics, machine learning, data management, supply chain analytics and data engineering.
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Determine how to leverage data science, machine learning, and other analytical techniques to offer actionable insights both internally & externally. Exposure to big data platforms - Snowflake, Redshift, Azure, Matillion, Hadoop.
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Must successfully complete competency-based interview with a focus on high performance computing disciplines (such as CFD, FEA, Molecular Dynamics, Weather Forecasting, Computational Chemistry, Reservoir (Seismic) Simulation, Media Rendering, Machine Learning, Financial etc.
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Additionally, we are dedicated to crafting exceptional data infrastructure and innovative solutions in the realms of data science and machine learning. Stay up-to-date with the latest advancements and trends in machine learning and data science and apply them to evolve the measurement system.
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In Disputes we build highly available and scalable, Machine Learning driven products that integrate with the broader Cash App ecosystem to help customers solve their problems. Your team will be responsible for one of three major work streams within the Disputes Team. You will work closely with the other work stream engineering teams, including Mobile Engineers, Product Managers, Designers and Machine Learning Engineers.
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machine learning source big science jobs Company: Walmart in San Francisco, CA
FEATURED BLOG POSTS
Recruiting in a Recession: Hard Truths That Talent Acquisition Experts Must Accept
The summer had economists from around the globe embroiled in a debate about a possible recession coming in the next few years (or months). As of October 2022, the U.S. Labor Department data put the current inflation rate at 7.7%. The recent layoffs in the tech industry are just the first of what is soon to be a string of cutbacks by companies looking to save costs. For recruiters, this means freezes in hiring and fewer openings. It will also include the uphill task of finding the best candidates for them from the coming influx of recently laid-off job seekers. Now is probably a good time to brace for tough times in the next few years in the talent acquisition industry. To survive and thrive recruiting in a recession, here are some hard truths you will need to accept.
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We’ve all heard how important it is to set professional and personal goals. Developing and establishing goals keeps us motivated and moving forward in life. But not all goals are created equal. If you’re chasing goals that are too lofty, you’ll end up disappointed when you cannot reach them. Setting goals that are achievable and measurable is the key to success.
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Why is email etiquette important? Let's imagine you're hiring for a new role, and you’ve just received the email below.
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"Nothing we do is more important than hiring and developing people."
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Talent acquisition is a multi-stage process where candidates undergo various application steps before getting hired. The unfortunate reality is that it is a labor-intense system, with the hiring manager and recruiter often handling all of the work on their own. Ask any one of them, and you will hear about the overabundance of applications and the demanding task of filtering through them to find the best candidates. The quality of talent suffers under the weight of all that work on one person's hands. It's not easy, but as many companies are starting to realize, there is a better way. The future of talent acquisition lies in collaborative recruiting!
4 Talent Acquisition Trends Going Into 2023
For better or worse, a side effect of the COVID-19 pandemic was a marked shift in talent acquisition practices worldwide. With the struggle to retain talent that began in 2020, companies have had to rethink recruitment strategies. The result has been new talent acquisition trends that are well on their way to becoming commonplace. These are the practices that are going to become even more widespread going into 2023.