DataScienceJobs.com’s Post

I have a hot tip for the industry! A senior "data engineer" with experience in your cloud provider of choice is absolutely a viable choice for an "ML engineer" role. Personally, I remember getting into data engineering partially as a stepping stone to get into a role that involved AI/ML. Now, as I look into both data and ml engineer roles, I see much less data and much more ml but the requirements are ridiculous. Where are these "ml engineers" supposed to come from? How many people do you think actually have 10+ years experience as an "ML engineer"? Did you ever hear the term "ml engineer" 10 years ago? Side note: I know where to find a whole bunch of engineers with experience with data lakehouses, GCP, airflow, dbt, marketing data, and the media industry. We were all dropped on New Years day and are trying to navigate a job market with some serious growing pains. #technology #ml #machinelearning  #ai #artificialintelligence #selftaught #techindustry #proofofconcept #proofofcompetency  #recruitment #engineer #gcp #airflow #vertexai #aiplatform #dbt

Hello Network :) #Hiring Position: Senior GCP Data Engineer (AI/ML) Location: Charlotte, NC (Onsite) Duration: Long term No: H1B & Green Card's  Share the Resumes to [email protected] AI Integration with GCP: • Integrate Vertex AI with Big Query for training machine learning models. • Deploy machine learning models using Vertex AI, including exporting, versioning, and monitoring. • Automate ML workflows using Vertex AI Pipelines  • Implement data versioning and tracking using Vertex AI Dataset service and metadata stores. • Set up monitoring and logging for AI models deployed on Vertex AI using Google Cloud Monitoring and Logging.   Vertex AI and Gemini Integration: • Integrate Gemini with Vertex AI for enhanced model training predictions. • Use Gemini for feature engineering in Vertex AI workflows. • Manage model lifecycle and version control with Vertex AI and Gemini integration.   Data lake with Big Query: • Design scalable data lake architectures using Big Query. • Optimize query performance in a Big Query-based data lake through partitioning, clustering, and materialized views. • Ingest data from various sources (e.g., Kafka, Pub/Sub) into Big Query. • Handle data security and access control in a Big Query data lake. • Implement data partitioning and clustering for efficient data retrieval in Big Query. #Hiring #GCP #DataEngineer #VertexAI #BigQuery #AI/ML #Benchsales #hotlist #Benchhotlist #Benchsalesrecruiters #C2C 

In today's tech-driven world, the roles of 𝗖𝗹𝗼𝘂𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 and 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 are among the most sought-after. While these professions may seem distinct, they share a surprising degree of alignment in skills and responsibilities. But just how much overlap exists? 💻 𝗖𝗹𝗼𝘂𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 are architects of the digital infrastructure, designing, deploying, and managing cloud environments. They work with tools like AWS, Azure, and GCP, focusing on automation, security, and optimization. 🔍 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀, on the other hand, dive deep into data to build models, analyze trends, and generate insights. They use programming languages like Python and R, and increasingly, they rely on cloud platforms to scale their machine learning models and process massive datasets. So, where do they intersect? 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴: Both roles require proficiency in Python for scripting and automation. 𝗖𝗹𝗼𝘂𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀: Data Scientists increasingly deploy models on cloud platforms, leveraging the environments set up by Cloud Engineers. 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮: Tools like Spark and Hadoop are common in both fields, with Cloud Engineers setting up the infrastructure and Data Scientists using it for analysis. Overlap Percentage? It's estimated that about 40-50% of the skills and tools used by Cloud Engineers and Data Scientists align, especially as the cloud becomes integral to data-driven decision-making. As the tech landscape evolves, the synergy between these roles will only grow stronger. Whether you're in cloud engineering or data science, expanding your knowledge in both areas can significantly enhance your career prospects! #CloudEngineering #DataScience #TechCareers #MachineLearning #CloudComputing #AI #BigData

Once-in-a-lifetime Opportunity 🚀 We are hiring an Engineering Manager in the Databricks Compute Platform team. 💡 Why join Databricks? 💡 The Data + AI market, which Databricks dominates, touches every single industry out there. Every modern enterprise is, or will soon be - a data company. Powering this global expansion of the data-driven enterprise is Databricks - poised to grow rapidly along with this market. But being a leader in this space is hard. Data gravity is real; the pace of innovation is dizzying. How do you build a convergent platform that is broad in its reach and deep in its ability to support innovation? We've built Databricks on open source and open standards using a truly ☁ multi-cloud ☁ approach - bringing the platform to where the data is. 💡 Why Compute Platform @ Databricks? 💡 Compute is at the core of what Databricks does - powering the multitude of our offerings across the full data & AI stack. We operate at unprecedented scale across all major public clouds, with customers across every industry and geography. We work on problems at the intersection of distributed systems, cloud computing, data processing, ML, and HPC. We place a high emphasis on building well and building to last. We take a first-principles based and empirical / data-driven approach to problem solving 'cause there are few parallels to the scale and function at which we operate. And we are only getting started. 💡 Still not convinced? 💡 Invest in yourself - work alongside, contribute to, and learn from the best of the best in systems and data! Personally, some of the most prolific people that I've admired throughout my career are now my colleagues. Nothing like a whiteboarding session with one of them to get your daily boost of learning - Atul Adya, Rohit Jnagal, Hong Zhang, Ihor Leshko, Lev Novik, Swee Lim, Steffen Grarup, John Reumann. cc Lucian Popa Ankit Batra Ronghua Zhang Vibhor Nanavati Archit Bansal Piyush Singh If you are interested, apply here: https://lnkd.in/gMtw-hKN

What's Google Cloud up to lately? Let's find out at #GoogleCloudNext in Las Vegas from April 9 to April 11. I hope to see you there! Google has highlighted 9 areas of focus, what's your fancy? 1. Al and ML 2. Application Developers 3. Infrastructure Architects and Admins 4. Data Analysts, Data Scientists, Data Engineers 5. Database Professionals 6. DevOps, IT Ops, Platform Engineers, SREs 7. IT Managers and Business Leaders 8. Productivity and Collaboration 9. Security Professionals #Cloud #AI #GenAI #InfoSec #DevOps #DevSecOps #PlatformEngineering #Productivity #Developers #DatOps #dataEngineering #ML #LLMs #Analysts SiliconANGLE & theCUBE David Vellante John Furrier Crawford Del Prete Matt Eastwood Dion Hinchcliffe Tim Crawford Maribel Lopez Lydia Leong Zeus Kerravala Richard Seroter Thomas Kurian Regina Hoshimi Alison Wagonfeld Rick Villars R "Ray" Wang Holger Mueller Anurag Agrawal

🚀Are you applying for role at Microsoft for Data Engineering role? 🚀 I often receive questions about the interview process and roles at Microsoft, so I wanted to clarify a few key points: ⁉️ 𝐀𝐫𝐞 𝐭𝐡𝐞𝐫𝐞 𝐧𝐨 𝐃𝐒𝐀 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬? At Microsoft, various data engineering roles have different expectations: SoftwareEngineer: You can anticipate questions on DSA, system design, and Azure technologies. Data and AI Consultant: The focus here is primarily on Azure technologies in data engineering, with no DSA questions in the interviews. ⁉️ 𝐇𝐨𝐰 𝐝𝐨 𝐭𝐡𝐞 𝐫𝐨𝐥𝐞𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐀𝐈 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐧𝐭 𝐝𝐢𝐟𝐟𝐞𝐫? The roles, projects, and responsibilities are essentially the same; it’s mainly a matter of title. As a Data and AI Consultant, you are effectively acting as a Data Engineer. p.s: It is Not comparing DataEngineer in IDC and Data and AI Consultant in GDC, It is about the roles of Data and AI Consultant ⁉️ 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐈 𝐚𝐩𝐩𝐥𝐲 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐀𝐈 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐧𝐭 𝐫𝐨𝐥𝐞 𝐚𝐭 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭? To apply, visit our careers page https://lnkd.in/gJDvysdp. If you find a match, feel free to send me the job ID and your resume for a referral! ✅ Save for future purpose and reshare to help others. Excited to discuss more about cloud data engineering and career growth! 💼💡 🔗 Follow Deepika Ramaraj for more content on #AzureDataEngineering #MicrosoftInterview #behavioralinterview #technicalinterview #interviewpreparation #DataEngineering #Azure #CareerDevelopment

Hello Connections!!  We are Hiring for GCP Data Engineer  Position :- Data Engineer Experience :- 3-5 years Notice period :- Immediate (or) 15 Days. JOB TYPE- REMOTE/Hybrid RESPONSIBILITIES AND QUALIFICATION - Design, build, and manage data pipelines and ETL processes using GCP services like Dataflow, Dataproc, and Pub/Sub. - Optimize data processing workflows for performance, reliability, and scalability. - Ensure data quality, consistency, and accuracy throughout the data lifecycle. - Architect and manage databases on GCP, such as Cloud SQL, Bigtable, and Firestore. - Implement data partitioning, sharding, and indexing strategies for optimal database performance. - Utilize GCP's storage services like Cloud Storage and BigQuery for efficient data storage and analysis.    If you are Interested Drop your Updated CV to               [email protected] #gcp #aws #azure #cloud #cloudcomputing #googlecloud #devops #technology #kubernetes #python #java #google #machinelearning #covid #it #programming #clinicalresearch #javascript #tech #datascience #software #d #bigdata #coding #persikkediri #linux #neurology #metaanalysis #amazonwebservices #gbp

How can data scientists leverage cloud computing for large-scale data analysis? Data scientists can leverage cloud computing for large-scale data analysis in the following ways: 1. Scalability: Cloud computing platforms provide the flexibility to scale computational resources up or down based on the data analysis needs. Data scientists can easily provision and allocate resources to handle large volumes of data, ensuring efficient processing and analysis. 2. Distributed computing: Cloud computing allows for distributed computing frameworks such as Apache Hadoop, Apache Spark, or Google Cloud Dataflow. These frameworks enable data scientists to parallelize and distribute data processing tasks across multiple nodes, significantly accelerating the analysis of large datasets. 3. Storage capabilities: Cloud platforms offer scalable and cost-effective storage solutions such as Amazon S3, Google Cloud Storage, or Azure Blob Storage. Data scientists can store and access large datasets directly from the cloud, eliminating the need for local storage infrastructure. 4. On-demand resources: Cloud computing provides on-demand access to powerful computing resources, including CPUs, GPUs, and memory. Data scientists can leverage these resources to perform computationally intensive tasks such as machine learning model training, simulations, or optimization algorithms. 5. Collaboration and sharing: Cloud platforms facilitate collaboration among data scientists by providing shared environments and tools for remote collaboration. Data scientists can share code, notebooks, and visualizations, enabling seamless collaboration on large-scale data analysis projects. 6. Cost efficiency: Cloud computing offers a pay-as-you-go model, allowing data scientists to optimize costs by provisioning resources only when needed. This eliminates the need for upfront investments in hardware infrastructure and provides cost-effective solutions for large-scale data analysis. By utilizing cloud computing, data scientists can overcome the limitations of local infrastructure, efficiently process and analyze large datasets, collaborate seamlessly, and achieve cost-effective scalability for their data analysis workflows. Do you need help writing your resume/CV, cover letter or optimizing your LinkedIn profile? Contact me now. Let us do this! Visit my website: www.yourresumeexpert.com #resume #resumewriting #resumewritingservices #cv #cvwriting #cvwritingservices #linkedinprofile #linkedinprofileoptimization #jobsearch #jobopportunity #jobvacancy #jobinterview #jobsearching #careergrowth #careerdevelopment #careeropportunities #interview #softwareengineerresume #datascience #dataanalyst #computerscience #executiveresumes

Important AWS services to learn for interviews and projects. Depending on the job, you need to go deeper in some more than others. For example, if you are going for ML/Data Engineer/Architect roles, you have to dive deep on the GEN AI and Analytics layer more than other layers. -------- If you like this and want to know more about system design, and AWS, please subscribe to my free newsletter - https://lnkd.in/eJevzpeM #systemdesign #aws #interview #career #techinterview