Azizi Othman’s Post

Writing a Good Job Description for Data Science/Machine Learning: Things to do and things to avoid in order to find the right candidates for your open position Photo of a very good candidate by Thomas Butler on Unsplash I’ve probably been involved in the hiring process for data scientists a dozen times or more over my career, while never being the hiring manager myself, and I have been closely involved in writing the job description for several of these. It kind of seems like this should be easy — you’re just trying to convince people to apply for your job, so you can pick the one you like best, right? Well, it’s actually more complicated than that. Most of the people out there in the world are not qualified for any given job, and even among those who are qualified, there may be reasons they wouldn’t like working in this role. It’s not a one-way street; you don’t want just anybody to apply, you want the best suited people, for whom this job would work, to apply. So, how do you thread that needle? What should you write?This column is only my opinion and does not represent the views of my employer. I have not been involved in writing any job descriptions my current employer has posted, for ML or anything else. Why write a Job Description? To figure out what to write, let’s break down what it is a good job description is supposed to do, for a DS/ML job or for any other kind. * Explain to candidates what the job is, and what they would do in the job * Explain to candidates what qualifications you’re looking for in applicants These are the bare essential functions, although there are several other things your job description posting should also do: * Make your organization seem like an attractive place to work for a diverse pool of qualified candidates * Describe the compensation, work circumstances, and benefits, so candidates can decide whether to bother applying With this, we’re starting to get into more subjective and complicated components, in some ways. In some spots, I’m going to give advice for two different scenarios: first, for a small organization with few or zero existing DS/ML staff members, and second, for a medium or large sized organization with some DS/ML staff. These two can be quite different situations, with different needs and challenges in certain areas.You may notice I’m using “DS/ML” a lot in this article — I consider the advice here good for people hiring data scientists as well as those hiring machine learning engineers, so I want to be inclusive where possible. Sorry it’s a little clunky. What is this job? Firstly, for any organization, consider what kind of role you have open. I’ve written in the past about the different kinds of data scientist, and I’d strongly recommend taking a look and seeing what archetypes your role fits into. Think about how this person will fit into your organization,… #MachineLearning #ArtificialIntelligence #DataScience

#AI #ML #Tech Writing a Good Job Description for Data Science/Machine Learning: Things to do and things to avoid in order to find the right candidates for your open position Photo of a very good candidate by Thomas Butler on Unsplash I’ve probably been involved in the hiring process for data scientists a dozen times or more over my career, while never being the hiring manager myself, and I have been closely involved in writing the job description for several of these. It kind of seems like this should be easy — you’re just trying to convince people to apply for your job, so you can pick the one you like best, right? Well, it’s actually more complicated than that. Most of the people out there in the world are not qualified for any given job, and even among those who are qualified, there may be reasons they wouldn’t like working in this role. It’s not a one-way street; you don’t want just anybody to apply, you want the best suited people, for whom this job would work, to apply. So, how do you thread that needle? What should you write?This column is only my opinion and does not represent the views of my employer. I have not been involved in writing any job descriptions my current employer has posted, for ML or anything else. Why write a Job Description? To figure out what to write, let’s break down what it is a good job description is supposed to do, for a DS/ML job or for any other kind. * Explain to candidates what the job is, and what they would do in the job * Explain to candidates what qualifications you’re looking for in applicants These are the bare essential functions, although there are several other things your job description posting should also do: * Make your organization seem like an attractive place to work for a diverse pool of qualified candidates * Describe the compensation, work circumstances, and benefits, so candidates can decide whether to bother applying With this, we’re starting to get into more subjective and complicated components, in some ways. In some spots, I’m going to give advice for two different scenarios: first, for a small organization with few or zero existing DS/ML staff members, and second, for a medium or large sized organization with some DS/ML staff. These two can be quite different situations, with different needs and challenges in certain areas.You may notice I’m using “DS/ML” a lot in this article — I consider the advice here good for people hiring data scientists as well as those hiring machine learning engineers, so I want to be inclusive where possible. Sorry it’s a little clunky. What is this job? Firstly, for any organization, consider what kind of role you have open. I’ve written in the past about the different kinds of data scientist, and I’d strongly recommend taking a look and seeing what archetypes your role fits into. Think about how this person will fit into your… #MachineLearning #ArtificialIntelligence #DataScience

Top 5 Data Science Jobs in 2024 In the Fast Paced world of Technology, Data Science has emerged as a Leading field people want to Pursue, offering Tremendous career prospects. With the increasing reliance on data driven decision making, the demand for skilled Data Scientists is at an all time high. If you’re considering a career in Data Science, it’s essential to explore the Top Job roles that will be in demand in 2024. This blog post highlights the Top 5 Data Science Jobs in 2024 that promise exciting opportunities and growth potential. https://lnkd.in/ek9RkJnP #Hiring #Jobs #AI #ML #AIJobs #MLJobs #AImployed #Machinelearning #AImployedLaunch #AIRevolution #FutureOfWork #AIJobs #CareerDevelopment #TechCommunity #JoinUs #UnlockYourPotential #AIEngineering #TechInnovation #DataDriven #AIInnovation #DigitalTransformation #TechLeadership #TeamCollaboration #AIPrototyping #GrowthConsultancy #TechCareer 

You need practical experience to get a job as a data scientist. True! You need a job to get practical experience in data science. True! Seems like a deadlock, right? But sadly, this is true. Today, companies are unwilling to try to train people in data science and start their requirements by mentioning prior experience in data science, even for beginner roles. But how can you gain the experience without working? If you just show courses and certifications in your resume, the hiring manager will reject it within 30 seconds. Even if you are great in algorithms and ML theory, you will not get the chance for an interview. Another problem. Many companies hire data scientists, but they mostly do reporting and bug fixing, so they never get any practical experience. After working for some time, they feel frustrated because the job is not up to their expectations. They also cannot switch jobs easily because they don’t have any notable experience. Many tried to fake it but got rejected in the screening round itself. So, the biggest question for every aspirational data scientist is: How can they break this cycle? How can they gain at least some practical experience, which they can put in their resumes and at least get a fair chance for an interview? The answer: Exposure to Enterprise-Grade Data Science Projects! You start with a problem statement, get the data needed, do feature engineering, apply models, and measure performance. Cool right? But you cannot do just any project. Your projects should be: - Unique (And not like iris classification or titanic survival) - Brings value - Impactful - Based on current market trends (Super Important) I have been researching this and found the perfect website - ProjectPro. They have some unique enterprise-grade projects that can be used for learning and portfolio building. Some of my favorites are: - Fine-tune Large Language Model for Advanced Chatbot - Langchain Project for Customer Support App in Python - Llama2 Project for MetaData Generation using FAISS and RAGs - MLOps Project to Build Search Relevancy Algorithm with SBERT - Build a recommendation engine like Amazon They have a list of 250+ projects and cover almost all the areas of data engineering and data science. Check them here - https://bit.ly/3w4vQVv They are not only projects but are explained perfectly, so you can describe them in interviews with all the technical details and reasons for selecting any specific model and evaluation metrics. That makes your profile strong for any relevant data science role. Share it with others! #datascience #machinelearning #nlp #llm #projecrts #ds #ai #ml #jobs

What specific aspects or questions do you have in mind about time series forecasting projects?

How to Become a Data Scientist in 2024 Data Science, a field that involves extracting valuable insights from the vast pool of data, is rapidly evolving and offers exciting Career Opportunities. Organizations worldwide rely on Data Scientists to help them make informed decisions, optimize operations, and drive innovation. In this comprehensive guide, we will walk you through a step by step roadmap on How to Become a Data Scientist in 2024. We’ll cover everything from the Qualifications and Skills required to the Career prospects and the Latest trends in the Field of Data Science. In today’s digitally driven world, organizations accumulate an unprecedented volume of data. This data is a goldmine of information waiting to be unlocked, and Data Scientists are the key to this treasure trove. They are the professionals who specialize in analysing and interpreting data, providing insights that guide strategic decisions and lead to innovations. https://lnkd.in/eVJ569bg #Hiring #Jobs #AI #ML #AIJobs #MLJobs #AImployed #Machinelearning #AImployedLaunch #AIRevolution #FutureOfWork #AIJobs #CareerDevelopment #TechCommunity #JoinUs #UnlockYourPotential #AIEngineering #TechInnovation #DataDriven #AIInnovation #DigitalTransformation #TechLeadership #TeamCollaboration #AIPrototyping #GrowthConsultancy #TechCareer 

 Excited to share my very first blog for AIQU: "Your Guide To Hiring The Best Data Scientist"! Key takeaways include: - Essential skills and qualifications - Tips for conducting effective interviews - Understanding the evolving role of data scientists in today's market Read the full blog here: https://lnkd.in/du3QPVUa #DataScience #Hiring #Recruitment #AIQU #TechHiring #DataScientists #UAE

6 In-Demand Data Science Jobs You Don't Want to Miss in 2024 The data science field is booming, offering exciting opportunities for those with the right skills and knowledge. Here's a look at six of the most in-demand data science jobs in 2024: 1. Machine Learning Engineer: These architects of the future design and build AI programs that can learn and make decisions based on massive datasets. They work closely with data scientists and programmers to create intelligent systems that automate processes and personalize user experiences. 2. Data Analyst: Data analysts are the information detectives of the data science world. They collect, clean, and analyze data to identify trends, patterns, and insights that inform business decisions. Strong communication skills are key, as data analysts need to translate complex data findings into actionable recommendations for stakeholders. 3. Business Intelligence Analyst: Business intelligence analysts leverage data to provide businesses with a clear picture of their current performance and future potential. They use data visualization tools to create dashboards and reports that track key metrics, identify areas for improvement, and support strategic decision-making. 4. Data Scientist: The data scientist role is the jack-of-all-trades in the data science field. They possess a blend of statistical, programming, and business acumen to tackle a wide range of challenges. From building predictive models to designing experiments, data scientists extract knowledge from data to solve complex problems and drive innovation. 5. Deep Learning Engineer: Deep learning engineers are the masterminds behind cutting-edge AI systems. They specialize in building complex models using deep neural networks, a form of artificial intelligence loosely inspired by the human brain. Their expertise allows them to create intelligent systems capable of tasks like image recognition, natural language processing, and self-driving cars. 6. Data Security Analyst: As the volume and complexity of data grows, so does the need to protect it. Data security analysts play a critical role in safeguarding sensitive information from cyber threats. They develop and implement data security measures, monitor for suspicious activity, and ensure data privacy compliance. What skills do you need to land your dream data science job? Regardless of the specific role, strong analytical thinking, programming proficiency (especially Python), and excellent communication skills are essential. Additionally, domain-specific knowledge can be a major advantage. Are you ready to join the data science revolution? Share your thoughts and career goals in the comments below! #DataScience #DataAnalytics #MachineLearning #AI #FutureofJobs #ITEBsAcademy

The skills that you need to develop if you want to be a data scientist are very different than the skills you need to develop if you are a data scientist. If you want to be a data scientist, focus on the following skills: ⚡Statistics ⚡Python ⚡Machine Learning using Scikit Learn ⚡Data Manipulation using Pandas, Numpy ⚡Data Visualization (Power BI) ⚡Data Modeling (Star Schemas) ⚡SQL If you are a data scientist already, focus on learning the following: ⚡Business Consulting ⚡How to Manage both Up & Down ⚡One Cloud Platform (Azure, AWS, or GCP) ⚡One Big Data Platform (Databricks or Snowflake) ⚡Data Manipulation using Spark, Polars ⚡AI Engineering ⚡Data Engineering ⚡ML Engineering For entry level jobs, companies want people who can do SQL queries, visualize data, create machine learning models, and perform statistical analysis. For senior hires, the market is moving more toward data / AI generalists who can do more than what is traditionally expected from a data scientist. Thus, it behooves you to either improve your people skills, improve your engineering skills, or both. Big data platforms are great for data engineering, cloud platforms are great for ML engineering, and AI engineering is where it's at if you want to create powerful cloud-hosted applications. #datascience #career #careeradvice

Solid tips for mid-career data scientists. #BreakingIntoDataScience #datascience #career