How to be a data Scientist

Hello - I am not a Data Scientist but have interviewed many of them, so I can speak to a couple things from the other side. To keep it succinct, some say there are three main pillars of data science: (1) statistics, (2) programming, and (3) strategy. I'll talk to each one below.

1. Statistics is a must-have and usually has to be demonstrated academically. Highlight in your resume any statistics-related courses you have taken / are taking in college or graduate school. If you can't list at least 3 advanced-level courses, you might want to consider pursuing an additional degree/certification before applying to more data scientist positions.
2. Programming is also a must-have and can be demonstrated either with academic/professional experience or through personal projects. Highlight in your resume any programming languages you can use, as well as your level of proficiency in each, any programming courses you've taken, and any personal coding projects you have posted online. Always be coding personal projects to both develop and demonstrate your skills, and be sure to make them available to potential employers on a personal website or Git repo.
3. Strategy is not something one is expected to master early in their career, but it's critical for being a successful data scientist. Programming and statistics will help you answer questions, but strategy will help you ask the right ones (which is actually more important)! If it were me, I would think about how to acquire this skill as you apply for jobs, and ask potential employers in your interviews about how they might help you in that quest.

Generally speaking, regardless of your level of education or experience, it's always a good idea to continuously be taking statistics, programming or general data science courses on sites like Coursera, Udemy, Udacity, etc. If you're unsuccessful in your search so far, it may be because other candidates are simply more qualified. The more courses you can take, in school or online, and the more personal projects you can build and post, the more qualified you'll appear until you get that next job.

Finally, if someone contacts you to let you know that you were not selected for a job, it can't hurt to ask for feedback. They might be able to shed some light on where you fell short, and if not, then at least you demonstrated the discipline and interest to improve.

Best of luck in your search!

Data Science is an evolving field and it can be hard to break into. There is no standard path or definition of what a data scientist is. It's hard to provide specific advice here, since I don't know too much of your background and what kind of jobs you are applying for. If you provide more information, happy to respond with specific advice.

It looks like you are doing well in Kaggle competitions, which is a great thing to highlight on your resume. I'd suggest also thinking more about specific data science problems you're interested in tackling and developing supplementary skills to make your application stand out.

If you are interested in analyzing business data and help companies utilize those insights to build better products, market more effectively, and reach wider audiences consider looking for business analytics, marketing analytics and business intelligence roles. Some will use the title business intelligence analyst or data science - analytics. Additionally, there is opportunities to take traditional marketing analytics and growth scaling roles and bring your data science background to generate novel value through running A/B campaigns, automating SEO optimization experiments, and using your data science skills to drive and evaluate conversion metrics.

If you're interested in working on the customer customer side, consider looking at retail companies like Wayfair, Amazon, etc, where you'll be building and tuning content recommendation models. Many traditional companies like Macy's, Nordstrom's and Walmart are building large datascience teams to uphaul their online shopping experiences and compete with the likes of Amazon. The fashion space is also a fascinating place for data science innovation with companies like Zolando and Stitchfix doing cutting edge research and building fashion recommendation services.

The list goes on. But each of these areas has a distinct set of problems, skills, and needs. By identifying a space you're interested in, you can narrow your focus to apply with greater precision for roles that interest you. You can also identify analogous data driven roles to break into the space or apply for traditional roles that you can innovate with your skills.

Outside of writing more targeted cover letters and resumes, I'd also emphasize the soft side of networking and informal interviews. Go on LinkedIn and look for people with your desired job title and request to 30 min of their time for a phone call to ask for thier advice and experiences. At the end of the call politely ask if they might be hiring and could refer you. Two of my jobs in this space can from cold contacting folks on LinkedIN and expressing genuine desire and interest to learn more about their career paths. And one even created a custom job to hire me when I didn't fit into the roles they had which in turn open the door for many other opportunities in AI and data science.

It looks like you're in SF. There are many meetups for networking and companies looking to hire Data Scientists. If you have trouble gaining traction at well established firms, try working at a VC backed startup for a couple of years in order to gain more experience and then transition back into larger companies. The nice thing about VC backed startups is that the community is pretty small and folks are being constantly poached and offered positions. Once you have have the role on your resume, it becomes a bit easier to move around.

Hope that's helpful.

Good luck!