¿Cómo muestra las habilidades de aprendizaje automático en su currículum?

A senior engineer will most likely benefit from having the work experience being showcased first where somebody just coming out of school will find it easier to display the education first. If the person has a "shiny" education (eg. "PhD at Harvard"), there is an argument to put it at the top of the resume, otherwise, experience always trumps education.

Its all about project - illustrate the problem that you face in the project - add how did you tackle those project? - Specify which particular algorithm the you used and why did you used it - You have to explain how did you reach to that particular - all of your project should be measured by some unit. Maybe an accuracy or error or something like that - Associate, the KPI of your machine, learning project with the business aspect

A glance at your resume should give a reflection of your skill-set, ability to own outcomes and be responsible for deliverables. Mentioning quantifiable metrics in projects like "Fine-tune ResNet-50 with some additional layers on a private dataset to improve classification of clothing images on our site by 7%" is a reflection that you know theoretical things about CNNs and have also applied them in a project. Senior folks tend to have such things reflecting the impact of their contribution.

I read a lot of CVs. ML engineers’ CVs usually differs from Software Engineers CVs. 1. Document formatting done in Latex (e.g. Overleaf is an excellent choice) 2. The most important sections are: education, experience (industry or academic), technology, projects, publications, volunteering, and languages. 3. Experience should contain achievements with exact numbers. 4. All information should be relevant to the interested position, so it is fine to have different versions of the CV for each possible position. 5. 2-5 pages long 6. The first page must contain the most important information with relevant keywords.

Incorporate specific projects where you applied machine learning techniques, highlighting outcomes and methodologies. Quantify results to demonstrate impact and showcase proficiency in relevant tools and languages. Additionally, mention any certifications, publications, or participation in competitions to further validate your expertise.

I hired data scientists and read thousands of resumes. The wrong way of highlighting your machine learning experience is just listing a bunch of keywords. There are lots of resumes of people that are specialists in Keras, TensorFlow, PyTorch, SciKit Learn, computer vision, NLP and a few other things, but few are really specialists. They just used some of these once or twice. Instead of this making you stand out, it will make you the same as almost everyone else. What I want to see but rarely see is someone that has a relevant accomplishment with metrics: something like "reduced seller fraud by 10.1% by using GenAI to create a chatbot that interviews sellers and ensures that they are legitimate". No keywords, but I know you're a specialist.

Each line in your resume is an opportunity to display relevant keywords. Be specific! There are so many kinds of "ML engineers". Are you a Deep Learning specialist with computer vision expertize or are your ML skills limited to logistic regression? That is the type of questions a hiring manager will try to answer by reading your projects. Readers will spend a few seconds doing this "analysis", so the keywords, skills and project involvement have to be crystal clear!

I’m not a resume expert but I think your projects and skills should be what you are highlighting. Burying it at the bottom of page 2 isn’t helping.

I have always been an applied ML researcher vs a ML engineer, so my advice might be a bit different from others. I normally list the key applications I have developed ML approaches for using bullet points. Additionally, I list selected publications and open source repos.

You have to give metrics and numbers on what your solution achieved and not take extra space writing how you built it as majority make the latter mistake.

That should be done but it is not the most important part of your resume. This should be displayed after the experience and education, and it is mostly another opportunity to put relevant keywords for automated parsers to capture additional signals to categorize the resume correctly.

The best place to list it is the experience section, where you list ML projects and experience. For example, providing information about RecSys projects, assuming you have expertise in building such systems and all related skills. Separately, you can concisely list ML tools and technologies connected with each position. Try not to repeat yourself. Keep your CV clean.

Create a dedicated skills section that highlights your proficiency in machine learning tools, programming languages (e.g., Python, R), and frameworks (e.g., TensorFlow, PyTorch). Include both technical skills (algorithms, data preprocessing) and soft skills (problem-solving, collaboration). This section acts as a quick reference for recruiters assessing your qualifications.

I possess expertise in machine learning, specializing in deep learning models like CNNs and RNNs, proficient in frameworks such as TensorFlow and PyTorch, with a strong grasp of data preprocessing, feature engineeringand and model evaluation techniques.

In my opinion, the most important thing to include in a resume is your projects and working experience. But if there is no experience about something, in such a case education qualification and certifications can be helpful. For example, as Machine Learning is a fast growing field, it may not be possible to experience everything by doing projects. In such a case, if you wants to show some specific skills in your resume, you can do it by using education qualifications or certifications.

As ML is a fast growing field and the course curriculum tends to lag a bit, showing some of the certifications earned or projects done to hone or learn new skills can reflect your agility to learn new things. Most importantly, if you've applied them in some project like "Earned a certification in training GANs" and applied in my current work where you used GANs to solve some problem shows that you can learn new skills and apply them.

Highlight your relevant education, such as a degree in computer science, data science, or a related field. Additionally, include any certifications you've obtained in machine learning, data science, or related areas. This signals to employers that you have a solid academic foundation and have invested in staying current with industry trends.

If you're starting your professional career in ML, education and all extra courses are essential. Don't list all possible courses; be concise. Group them by topic. It will show your ability to learn quickly. If you're an experienced ML engineer, focus on the experience section instead.

I would have a different strategy that the one described here. Know the types of jobs you are ready to apply to (NLP specialist, MLOps specialist, ...) and have a more focused resume for each job category. You may use the same resume for two different categories if they are close enough. The reason is that to get one job, you need to apply to 200 of them. That is a number game! You cannot tailor your resume to each job, but you can have a few ready such that you can apply fast with a relevant resume to each. Applying to jobs is a job on its own, so you cannot waste time in such details.

It is reasonable to adjust your CV for specific job descriptions and have multiple versions of your CV. When the resume is ready, re-read the position description and evaluate your CV once again. Check the listed experience and skill set. It should be relevant to the job description.

Tailoring resume to the job description is crucial for catching the recruiter's eye. Highlighting relevant skills and experiences that match the job requirements increases chances of securing an interview. It showcases alignment with the company's needs, demonstrating suitability for the role.

Customize your resume for each application by aligning it with the specific requirements of the job. Use keywords from the job description to emphasize your suitability. Tailoring your resume demonstrates attention to detail and a genuine interest in the role, making it more likely to pass through applicant tracking systems.

When writing your resume, make sure to present yourself as a "leader". When reading resumes, it is easy to see if somebody just did what they were told or if they took the initiative to push impactful projects forward. A leader will present projects from a business prospective (ie. with the monetary impact of a technical project) and team prospective (eg. how many people did you have to coordinate to lead a project to success?). A business minded engineer makes money to a company and a team minded engineer is aware of the people complexity of software projects. Engineers that just do what they are told are easily replaceable!

To showcase your machine learning skills on your resume, create a dedicated "Machine Learning Skills" section where you list key tools and libraries along with proficiency levels and it should be very short and crisp. Highlight your machine learning projects concisely in a "Projects" section, outlining problems solved and outcomes achieved. Mention relevant certifications and courses that demonstrate your learning commitment. Include a link to your GitHub or portfolio for in-depth exploration.

You can display your M/L skills in any of the described approaches- I am looking for what you achieved in the projects and how did they impact your company?

Demonstrate Effective Communication, Showcase Problem-Solving Skills, Highlight Interdisciplinary Collaboration, Publications and Contributions, Quantify Impact. By incorporating these additional points, your resume portrays your technical proficiency and emphasizes your ability to communicate effectively, collaborate, and contribute to the broader machine-learning community. This holistic approach makes you a more compelling candidate for a data science role, aligning with the multifaceted nature of machine learning in real-world settings.