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Familiarity with AI (Artificial Intelligence) subsets such as Machine Learning, Large Language Models (LLM) and Natural Language Processing (NLP). Strong demonstrable experience using Dart in building Flu t t e r consumer-facing applications.
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Using natural language processing (NLP), machine learning (ML), Generative AI, and other relevant AI technologies and platforms; Understanding or hands on experience with Lex, Nuance Mix, Dialog Flow, AWS and Google Cloud, R, Power BI, Tableau, Alteryx.
Full-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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Drive the development of a comprehensive generative AI SDK, integrating Large Language Models like OpenAI and Google AI with popular programming languages such as Python, Java, c# etc., using platforms like AWS Bedrock or Azure Semantic Kernel or Google's Vertex AI.
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Ability to read, understand and script/code for data analysis, optimization, or regression (e.g. Python, R, MATLAB, STATA, Perl, PL/SQL, T-SQL, C#, Javascript, or GAMS. Contribute to artificial intelligence/machine learning (AI/ML) and data modeling projects.
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Our researchers have a broad range of expertise related to computer science and electrical engineering, such as AI/ML, algorithms, digital signal processing, audio engineering, image processing, computer vision, data science & analytics, distributed systems, cloud, edge & mobile computing, computer networking, and IoT.
$144,600 - $176,700 a yearFull-timeExpandUpdated 4 days ago - UpvoteDownvoteShare Job
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Proficient in data science tools and programming languages such as Python, R, Scala, SQL, and SAS. Work with a variety of data science tools and programming languages such as SAS, Python, R, Scala, SQL.
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Experience working with cloud based data warehouses like Google BigQuery, Redshift, or Snowflake. Create new reporting and dashboards, data infrastructure, and queries to track key marketing metrics across multiple data sources leveraging SQL, R/Python, Looker, Tableau, and other visualization tools; monitor for changes in trends, share insights and make recommendations.
$152,000 - $206,000Full-timeExpandUpdated 2 days ago - UpvoteDownvoteShare Job
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Data Architecture & Tools: modern cloud data platforms (Snowflake, Databricks, Big Query, RedShift), data analytics tools (Tableau, Looker, Power BI), data programming models (DataFrames, pandas), and AI (Python, R, Jupyter Notebooks, data wrangling, machine learning.
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Proficient with the core concepts of at least one programming language such as Python, R, and SQL and adept with machine learning frameworks such as TensorFlow and PySpark.
$119,025 - $198,375 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Entry-level software programmers, Java full-stack developers, Python/Java developers, Data analysts/ Data Scientists, and Machine Learning engineers. We want Data Science/Machine learning/Data Analyst and Java Full stack candidates.
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Title- Lead/Sr Machine Learning Engineer - AWS (with LLM Focus) Programming Skills: Proficiency in Python and experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation), REST API frameworks (e.g., Flask, FastAPI), and LLM libraries (e.g., Hugging Face Transformers.
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M.S. degree or higher in Computer Science, Software Engineering, Data Science, Machine Learning/Deep Learning, Math, Physics or any related field. The principal Machine Learning Engineer at WGU is both a visionary leader and a hands-on builder.
$197,000 - $305,300 a yearFull-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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Experience with Optimization, Natural Language Processing, or Reinforcement Learning are preferred. 1-3 years of practical experience with data mining and analytics, database structure and ETL, predictive modeling, machine learning, and statistical programming languages.
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Proficiency in SQL and at least one programming language, such as R or Python, for data manipulation and analysis. 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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Specialized experience for this position must include: Experience with data science and analytical methods from conducting machine learning, Natural Language Processing, and technical procedures such as, solution design, implementation, and deployment.
ExpandUpdated 1 month ago
machine learning natural language processing python r data google cloud jobs in Atlanta, GA
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One day it's a covert statement to a mother returning to work after maternity leave. Another day it's a lingering gaze at an employee enjoying a culturally rich meal. These microaggressions (or sometimes macroaggressions) can take an employee from a confident, high-performer to one that feels insecure being themselves at work. Your employees engage with people with different ideas and feel most comfortable and valued when they can work without losing their cultural, racial, and gender identity. While most employers know this, why have workplace racism and sexism often been neglected?
When Rage Applying Strikes: How to Identify Unserious Candidates
As the job market remains highly competitive, we have seen a surge in "rage applying." This is when candidates apply to multiple jobs, often without considering whether they are truly interested in the role. Rage applying goes hand-in-hand with quiet quitting. Often, employees want to entertain the thoughts and feelings of leaving their job, but they aren't necessarily serious about leaving yet. Meanwhile, other employees engaging in this trend are actually trying to find a better role. As a recruiter, it can be hard to identify who are the real applicants in a sea full of quiet quitters, but understanding rage applying and identifying red flags will certainly help.
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In today's competitive job market, writing quality job ads is critical for attracting top talent to your organization. While networking and candidate referrals are prime real estate for finding qualified candidates, nothing beats the tried-and-true method of writing an extraordinary job ad. But while writing a great job ad is the first step, what's more important is increasing visibility. You could have the most detailed, well-written ad on the internet, but if no one sees it, then you are wasting time (and potentially money!). Employers often believe that job boards are the root of the problem, but you can learn how to increase job ad exposure by tweaking a few steps of your recruitment process.
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Building a Candidate Pipeline Through Internships
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.
Hiring Transparency
Transparency in hiring refers to the open and honest communication and information sharing that takes place between employers and job candidates. It encompasses all aspects of the hiring process, from posting job descriptions to providing feedback on performance during and after the interview process. In today's job market, hiring transparency has become increasingly important for both employers and candidates alike.