Mavent Analytics’ Post

Roadmap to become a highly skilled Entry Level Data Scientist and AI Engineer I'm excited to guide you from my professional experience and insights for you to take next big leap. I am conducting a free live webinar Wednesday 6th March @ 8.00 PM (IST) I will be covering following topics: 1) A Proven Pathways to Excel as a Highly Skilled Entry Level Data Scientist and AI Engineer. 2) Insights into the 'Data Science and AI Program.' 3) Major industries to focus on and specific teams to target 4) Roadblocks and major mistakes done by fresher’s who are looking for data science opportunities 5) Identifying Dos and Don'ts throughout the entire job search process within the Data Science industry. 6) Addressing the Frequently Asked Questions by Aspiring Data Scientists. If you want to upskill and know how it can help you out, then this webinar is for you. 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐟𝐨𝐫 𝐰𝐞𝐛𝐢𝐧𝐚𝐫 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐛𝐞𝐥𝐨𝐰 𝐥𝐢𝐧𝐤 - https://lnkd.in/gfKDeCJA Looking forward to connecting with you during the session! #careergrowth #careerdevelopment #datasciencejobs #aijobsearch #careerboost #careerguidance #jobtransition #jobseeksers

Roadmap to become a highly skilled Entry Level Data Scientist and AI Engineer I'm excited to guide you from my professional experience and insights for you to take next big leap. I am conducting a free live webinar Sunday 3rd March @ 11.00 AM (IST) I will be covering following topics: 1) A Proven Pathways to Excel as a Highly Skilled Entry Level Data Scientist and AI Engineer. 2) Insights into the 'Data Science and AI Program.' 3) Major industries to focus on and specific teams to target 4) Roadblocks and major mistakes done by fresher’s who are looking for data science opportunities 5) Identifying Dos and Don'ts throughout the entire job search process within the Data Science industry. 6) Addressing the Frequently Asked Questions by Aspiring Data Scientists. If you want to upskill and know how it can help you out, then this webinar is for you. 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐟𝐨𝐫 𝐰𝐞𝐛𝐢𝐧𝐚𝐫 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐛𝐞𝐥𝐨𝐰 𝐥𝐢𝐧𝐤 - https://lnkd.in/gfKDeCJA Looking forward to connecting with you during the session! #careergrowth #careerdevelopment #datasciencejobs #aijobsearch #careerboost #careerguidance #jobtransition #jobseeksers

⭐️ Professional AI, Data Science & Machine Learning Talent Solutions ⭐️ 🗣 I am going to be straight to the point, I want to speak with any business currently seeking AI, Data Science & Machine Learning professionals in Europe to join your team. I understand most companies have organic methods in place to recruit but I also understand how competitive and difficult it is to find skilled professionals within these spaces. 🎓 To give you some background on myself, I founded Go Tek in 2022 and began specialising in the placement of software (mainly Golang) professionals across Europe. Go Tek quickly became the “go to” recruiter within the world of Golang and we made well over 100 permanent placements in 2022 alone. ⚙️ Over the past 6 months my focus has naturally shifted towards AI, Data Science & Machine Learning due to the clientele we have obtained. I have again quickly built a reputation for providing a seamless, no-nonsense recruitment experience which my clients love. 🤝 I am not looking for quick wins, It’s not what me or my business stands for. We prefer to establish longer terms relationships that will essentially allow us to grow with our customers. I have 50+ recommendations on LinkedIn and over 29,500 LinkedIn connections. I didn’t get to these numbers by doing an average job. 📍My previous AI, Data Science & Machine Learning placements include; - Principal Machine Learning Engineer - Senior Machine Learning Engineer - Senior AI Engineer - Senior AI Solutions Engineer - Machine Learning Team Lead - Principal Data Scientist - Data Science Tech Lead - Senior Data Scientist ⭐️ If you are looking for a talent acquisition partner that will truly understand your needs, provide a seamless end-to-end process, and represent your business as if we work for you. We need to speak. 📧 cwilliams@gotekrecruit.com #MachineLearning #AI #DataScience #Recruitment #GoTek#NDCTek

Last week I posted a poll to find out what everyone thinks the hot job in Analytics in 2024 will be. The result was interesting and not what I predicted! It was a dead tie. 35% of votes were for AI Engineer, and 35% were for Data Engineer. With demand rising in organisations for candidates with experience in LLMs, NLP, & Generative models across industries such as legal, healthcare, biosciences (to name a few) & the rise of organisations offering AI services - I shouldn't be surprised. Concurrently, businesses continue to struggle to recruit for data engineers. The common themes are that whilst businesses receive a high number of direct applications the over-stuffing of key words on CVs marks a contrast in the technical prowess a candidate demonstrates at interview whilst also lacking the appreciation of commercial value that can be added through data. The 'war for talent' in data engineering seen in 2023 continues in 2024. As of right there are 1,032 live jobs for 'AI Engineer' on LinkedIn & 2,345 live jobs for 'Data Engineer'. That can be taken with a pinch of salt as some will be reposts by recruiters of the same role, and LinkedIn may pick up on the key wording in job descriptions where the title does not match either of the above. Equally - I have always been of the opinion that job titles in this space can be taken with a pinch of salt. What it does invariably reveal is that advanced Python & SQL skills will continue to be the most in-demand technical skills sought after by organisations looking to scale their data & analytics business units (shock). Perhaps the scales will tip as the year goes on and the number of live vacancies in AI will out number that in Data Engineering! #data #artificialintelligence #python #SQL #recruitment

⭐️ Professional AI, Data Science & Machine Learning Talent Solutions ⭐️ 🗣 I am going to be straight to the point, I want to speak with any business currently seeking AI, Data Science & Machine Learning professionals in Europe to join your team. I understand most companies have organic methods in place to recruit but I also understand how competitive and difficult it is to find skilled professionals within these spaces.   🎓 To give you some background on myself, I founded Go Tek in 2022 and began specialising in the placement of software (mainly Golang) professionals across Europe. Go Tek quickly became the “go to” recruiter within the world of Golang and we made well over 100 permanent placements in 2022 alone. ⚙️ Over the past 6 months my focus has naturally shifted towards AI, Data Science & Machine Learning due to the clientele we have obtained. I have again quickly built a reputation for providing a seamless, no-nonsense recruitment experience which my clients love. 🤝 I am not looking for quick wins, It’s not what me or my business stands for. We prefer to establish longer terms relationships that will essentially allow us to grow with our customers. I have 50+ recommendations on LinkedIn and over 29,500 LinkedIn connections. I didn’t get to these numbers by doing an average job. 📍My previous AI, Data Science & Machine Learning placements include; - Principal Machine Learning Engineer - Senior Machine Learning Engineer - Senior AI Engineer - Senior AI Solutions Engineer - Machine Learning Team Lead - Principal Data Scientist - Data Science Tech Lead - Senior Data Scientist ⭐️ If you are looking for a talent acquisition partner that will truly understand your needs, provide a seamless end-to-end process, and represent your business as if we work for you. We need to speak. 📧 cwilliams@gotekrecruit.com   #MachineLearning #AI #DataScience #Recruitment #GoTek #NDCTek

Data Science Career Insights from a Google Data Science Leader! I recently had the opportunity to converse with a seasoned data scientist with 10+ years of experience in the industry. She is the first data science professional I have met, who has been in the data science domain throughout her professional career! So listen carefully and read till the end for the opportunity to connect with DS Leader at #Google! Thank you, Manisha Arora for the insights. Career Background: Manisha currently leads the data science team at Google Ads, where her team focuses on building solutions in ad measurement for the largest advertisers. She leverages AI/ML and Experimentation to build scalable solutions that drive ad insights. To improve callbacks: ⭐ Targeted applications #Data Science is a broad domain with multiple titles such as applied scientist, product data scientist, research scientist, ML engineer, and more. Understand which role you want to pursue for long-term career growth and focus your efforts there. Manisha has a detailed blog on various DS roles and their skills, which is a must-read: https://lnkd.in/dyMTdt56 Additional tip from Me: If you have experience across different roles - build a resume for each. This way you can focus on both quality and quantity of applications! ⭐Recruiter connect Most large tech companies have specialized recruiters for various roles/levels. If you are a new grad, reach out to a University Recruiter. If you are an experienced professional, reach out to a Analytics/Data Science recruiter. To land your next role: Solid technical skills are table stakes to build a career in data science. The key ones to focus on are - ⭐Programming: Have a solid understanding of #SQL and #Python (#pandas). For DSA - knowledge of arrays, strings, and dictionaries is a must. ⭐#Statistics & AB Testing The ability to design statistically robust experiments and perform causal analysis to narrow down the impact of product changes is extremely important ⭐Machine Learning Focus on understanding core concepts - converting problem to #ML formulation, knowing the model assumptions, which model/metric to use when, gathering and cleaning data, and finally leveraging #data to solve business challenges. How to Stay Relevant? It is important to upskill yourself as the industry is going through a major shift in AI. To learn how you can upskill yourself and stay relevant in the industry, check out this Lightning Lesson, where Manisha will be talking about the latest trends and sharing key resources. https://lnkd.in/dbTC6r3V #datascience #hiring #dataanalytics #machinelearning #interviewprep

NOTE – this is a bit of a self-serving post/editorial I recently read a post (in LinkedIn) from a corporate recruiter sharing they received over 400 plus resumes for posted data science role. I’m not sure if this is normal but seems to me to be a lot of interest in the role. For reference, the role is U.S. based though half the resumes were from international candidates. Based on this recruiter experience and of those who commented to the post I have a few takeaways: 1) Lots of "data science/machine learning people" looking for roles – this is includes “classically” educated with Computer Science, Data Science, Data Analytics degrees as well as people with a tech background who are pivoting/trying to pivot into this space. So, junior or “newbie” candidates. 2) Employers are looking for/want experienced people not newbies nor junior folks.  3) Experienced Data Science/Machine Learning/ Data Analytics candidates are limited. 4) This provides a potential opportunity for companies to hire new/junior/ folks pivoting to Data Science/ML/AI and skill them up. Tie benefits to performance - ie stock option vesting and other incentives which encourage the candidate to stay at company once skilled up. This provides the company ROI on the "risk" in hiring a new to ML/Data Science person With the last bullet point in mind, I see a large number of Data Science/Machine Learning roles posted within LinkedIn – global locations. The vast majority of these job descriptions describe the ideal candidate as someone who has a Master’s Degree or PhD in Computer Science, Mathematics, Data Science with several years’ experience in the same discipline. Considering the number of open positions (listed in LinkedIn) I wonder where candidates with these backgrounds are coming from. To fill this gap of talent, if there is one, companies should consider building programs that skill up internal and external talent to meet the companies’ needs. Example – new hires, who have limited Data Science/ML experience and background, are required to take and pass identified courses that increase the new hire’s knowledge and skills.  I say this with the complete understanding of the importance of candidates understanding the principles of Data Science/Machine Learning – linear algebra, a bit of calculus, statistical modeling, Python, R, algorithm understanding, deployment methods, cloud platforms (Azure, AWS, Google Cloud), data structures, etc. As we, the collective economy, moves forward there will be an increase in the need for job candidates (internal and external) to have the above referenced skills across all orgs within a corporate entity. Why not build out an onboarding process that upskills employees – this increases the employee’s skill set while meeting the needs of the company. What do you all think? Am I crazy? Am I missing something? Please share your thoughts. 

🔍💡 Unveiling the Landscape of Data Science Careers: A Deep Dive into Today's Job Market 💡🔍 Hello, LinkedIn family! Today, I embarked on an insightful journey through a dataset encompassing the data science job market. It was an enlightening exploration, revealing not just the current trends but also the future direction of data science careers. Here’s what I discovered: 📊 Diverse Opportunities: The data science field is bursting with opportunities, from roles in AI and machine learning to analytics and data engineering. The diversity in roles shows the dynamic nature of this field and its integral part in shaping every industry. 📈 Skill Demand Dynamics: Analyzing job descriptions, I noticed a significant demand for skills beyond traditional data management. Proficiency in machine learning, deep learning, natural language processing, and visualization tools like Power BI and Tableau is becoming increasingly valuable. It’s clear that the data science skill set is evolving. 🌍 Geographical Hotspots: The dataset highlighted several hotspots for data science careers, with cities like San Francisco, New York, and Bangalore leading the way. However, the rise of remote work is rapidly democratizing where data professionals can live and work, expanding opportunities globally. 💼 Industry Verticals: Data science is no longer confined to tech companies. Healthcare, finance, retail, and even non-profits are seeking data professionals to drive decision-making processes and innovation. This cross-industry adoption underscores the universal relevance of data science. 👩🎓 Education vs. Experience: One intriguing aspect was the balance between educational qualifications and practical experience. While a strong educational background is valuable, real-world experience through projects, internships, and relevant tools usage is equally, if not more, critical in landing a job. 🚀 Future Trends: Looking forward, the integration of data science with emerging technologies like quantum computing, IoT, and edge computing hints at an exciting future. Continuous learning and adaptation are the keys to thriving in this ever-evolving landscape. I'm thrilled to have dived into this dataset and emerged with a broader understanding of the data science job market. Whether you're a seasoned professional or aspiring to enter the field, the future is bright, and the opportunities are limitless. Let's continue to learn, grow, and innovate together. Data science is not just about analyzing data; it's about unlocking the potential within data to shape our future! #DataScience #JobMarketAnalysis #CareerInsights #FutureOfWork #LearningAndGrowing #dataanalytics #powerbidashboard #powerbi #powerbideveloper

I am really trying to understand what kind of title I need to put into LinkedIn so that I don't get #recruiters reaching out to me for exciting entry level roles! I mean with over 20 years of experience at least bring up roles that aren't entry!! Here is the #genai job description that they must be using: Headline: Data Scientist (Yes, Really Entry-Level!) Sarcastic emoji Body: We're looking for a highly motivated and experienced (think 20+ years!) Entry-Level Data Scientist to join our growing team! Are you a recent graduate with a passion for data and a strong understanding of complex statistical concepts? Do you have 2 decades of professional experience applying machine learning algorithms to solve real-world problems? If so, you might be the perfect fit! (followed by several eye-rolling emojis) Responsibilities: Perform complex data analysis using cutting-edge tools and techniques (that we'll hopefully train you on). Develop and implement machine learning models to solve critical business problems (with limited guidance, of course). Communicate insights effectively to both technical and non-technical audiences (because apparently entry-level = senior communication skills). Qualifications (The Real Requirements): PhD in Data Science or related field (or maybe time travel to get 20 years of experience?) 20+ years of experience in a similar role (we're looking for a seasoned pro, not a newbie!) Expertise in all major programming languages (because why not ask for everything?) Experience with every data science tool imaginable (including ones that haven't even been invented yet!) A strong understanding of advanced statistical concepts (because apparently entry-level means genius level) Excellent communication and interpersonal skills (to explain why this job description is nonsensical) Bonus Points: Can successfully predict the future (because apparently this role requires psychic abilities) Fluent in all human languages (for international collaboration, of course) We offer a competitive salary and benefits package! (But seriously, who would apply for this?) #datascience #entrylevel #experiencedata scientist #weknowthismakesnosense #weneedbetterrecruitingpractices [Company Name] Link to Apply (Although why anyone would click this is a mystery) Disclaimer: This post is meant to be humorous and highlight the absurdity of job descriptions that ask for unrealistic qualifications.

Over the past month, I've been on the hunt for new job opportunities in the data field. It’s been three years since my last job search, and although that doesn't seem like a long time, I've noticed significant changes in the market. One might say different data job titles might have different requirements, but in my recent job search, I found common threads across various positions. Previously, the main technical skills employers sought were proficiency in Python and libraries like scikit-learn. While these skills are still important, there's now a stronger emphasis on cloud platforms and infrastructure tools. In my recent job interviews, I was frequently asked about cloud platforms like AWS, Azure, or even GCP and infrastructure tools such as Terraform. Knowledge of business intelligence tools like Power BI has also become a significant asset. The role of a data scientist used to be mainly about building accurate models. However, the job landscape has evolved, with roles now demanding more comprehensive skill sets. There's a growing emphasis on deployment skills, understanding how to deploy models in production environments, and software engineering skills to build scalable ML solutions. This shift reflects the industry's need for professionals who can not only develop models but also integrate them into broader systems. In interviews, I've noticed a decline in questions about traditional data science concepts like parameter tuning, overfitting, feature encoding, ... etc. Instead, there’s a growing interest in deployment strategies, the impact of GenAI and RAG on modern solutions, and even understanding and working with vector databases. To stay competitive in this evolving job market, it’s crucial to expand your skill set beyond traditional data science skills (almost all of us know that, but not all do that!). Gain proficiency in cloud platforms, infrastructure as code, and deployment strategies. Additionally, embrace new tools like business intelligence platforms and keep up with emerging technologies such as vector databases and generative AI. Diversification is great! Recognize that data roles are evolving, and be prepared to demonstrate a blend of data science, software engineering, and business analysis skills in your job applications. #data #job #ai #cloud