The PhillyTalent at its core answers this problem: a great portfolio matched with a good strategy. Let’s go over it very briefly.

Most serious job applicants in data science have similar qualities: They usually have a 4 year STEM degree, under 2 years of professional working experience and a lot of ambition. It’s a very competitive market and will continue to be so a succusfull candidate will need a very clear and thoughtful strategy. The keyword here is “competition”. It’s true that Data Science jobs are one of the hottest jobs available now. And most reports show they will continue to be so. Every year about 40,000 new competition is created from the top schools in the US alone. That’s why this question matters and our work at PhillyTalent is valued and supported by many. The competition is growing as mentioned and with it comes specialization of talents, broad job descriptions by companies, fake job postings by recruiters and over-promising resumes by many. Essentially a lot of noise.

“Better be playing Chess and not Checkers”

Every Talent should start their mentorship relationship with their mentors with the question “what are my competitive advantages”. This is going to be a list in most accepted applicants. You current location? Or the fact that you don’t have to pay rent because you are living with your parents? Your Premium LinkedIn? Your rich committed GitHub repo?

You are competing with a lot of talents with almost identical backgrounds, an undergraduate degree in Statistics, CS and one or two internships and co-ops. So, every single advantage counts but how they are played into your job hunting strategy is more important: And that is where an invested Mentorship relationship comes into play. Your strategy can still be working with recruiters. Or taking Data Camp classes while still working fulltime at your current job. Or attending online and on-campus job fairs. Perfecting your actual resume, your interview skills, your professional network, or your LinkedIn profile and your GitHub repos.  You need to clear your mind and self-evaluate your strategy and not your odds of winning. PhillyTalent Mentors help you with a lot of these. PhillyTalent team also help you with making sure you have a minimum requirement before talking to any mentor.

“Industry Experience is Key”

This one is tricky and where PhillyTalent really takes it home. Remember the competition? That is the “Market” side. That’s for you as the talent and you have almost full control over your skills and value proposition. The other side is the “Demand” for the Data Science role. And that is out of your control.

Let us explain this further:

Another way to look at it is this: No matter how good you are, someone else should make you an offer first at the right time and place. And that’s out of your control. The company you were so close to land the job might decide to fire the recruiting company you worked with and you don’t even know. That’s one scenario but there are a lot of cases that are chalked to “culture fit” that you can’t help but accept the decision. Because you don’t have the industry or company insight. In the end, all you get is “we decided not to move on with your application at this point” email.

This is true for any job but we argue that for DS jobs the lack of ‘Industry Experience’ is especially crippling to young candidates with no prior professional DS experience because there is already an assumption that says “She is technical but is she going to be able to add value to our business”.

A front-end developer seeking a Design role doesn’t have to deal with this assumption. A certified SysAdmin applying for remote jobs at RedHat doesn’t have this challenge. No recruiter will say “this seems to be a certified SysAdmin but we are not sure if he can add value using our existing systems”. But with Data Science roles, a recruiter can always question a candidate experience on delivering data-based products for a particular industry, maturing its process and building other products around it. Even if you work with off-the-shelf data modeling tools this is still a valid concern for any business. This is still a valid concern if the DS role is about automating an existing process using off-the-shelf ML tools.

So in order to get a professional DS job, one needs to have a professional job?

What should a recent CS grad do about this chicken-and-egg problem? One has to educate themselves about the industry and the companies who are actively recruiting as much as possible.

Now, let’s go back to the Market and Demand conversation. PhillyTalent helps you answer this question: who is building up this “Demand” for you as the DS talent of today and where is the bar for you? What do you need to pass? We believe no one can better help you other than an existing DS professional and this connection is what we are aiming to build in Philadelphia.

Example: Recent Drexel grad living in Philadelphia: Pick healthcare as your industry and network with professionals from the industry. Try all Data Meetups. Network with BIC, Jefferson Hospitals and Chops IT staff and PMs (They don’t have to be in DS field). After a few weeks of training and getting “job-ready” your resume starts to match few recent hires and you are updating yourself by reading on ArXiv, AirBnB DS medium posts and GitHub repos you have found off of from Google DS engineers. Ask your network if you qualify for the DS role they have before applying. Your Industry contacts tell you Fraud Detection and building pipelines with APIs like H2o on AWS is what they “think” they need and you build few models on this topic with some public data you find from Kaggle. While on Kaggle, you start submitting models on Kaggle and build your first R CRAN package. Show your model to your contacts and ask them if they can have a look at it. Keep this up until they said you are job-ready. What they say is what it takes.

Our classes are hands-on remote sessions that enable one technical mentors to work as much time with candidates as possible. The capacity of each class is 12 work with 3 mentors over the span of 10 weeks. Furthermore, you’ll be working in pairs to gain more experience in the code versioning and software development life cycle. By the end of the class, each candidate and class member will demo their model for all mentors. Selected demos will be shared with our growing commnity of data scientists.

We recommend anywhere from 1 to 4 hours a week over a 6 to 14 week period, but this is completely up to you and your mentee. You’ll be able to negotiate a time commitment and mentorship period duration that works for you both during your interview with each candidate.

Yes. You can mentor as many people as you want, though we recommend a maximum of 4 for most mentors who are doing this part-time.

Many of PhillyTalent mentors already are offering this service for friends and family free of charge but as a PhillyTalent mentor, you will take the same time commitment and turn it into a revenue-generating commitment for yourself and your family. Mentors typically take home between 3% and 8% of their mentees’ first-year salary.

At the moment the median salary of placed mentees in DS in Philadelphia is $70,000. However, you’re free to set your percentage rate as you see fit. Some factors to consider when doing this are: your weekly time commitment, the duration of the mentorship, and what works best for you on a case by case basis according to any particular technical mentee level and other factors.

Yes. We have five types of mentors and not all our mentors are required to be in the US. For the differences between each type please check our job portal page or contact us at

You’ll be helping your mentee to build a portfolio on GitHub by helping your mentee join and compete on at least 3 different data competitions. You will also be assigning your mentee road maps for finishing up on our training contents and have them develop these skills with a hands-on approach.

Mentees generally get three things out of the mentorship: technical skills (which they build through the project you’ll guide them through), interview preparation and job hunting advice (we’ll provide support by setting them up for practice interviews), and networking support.

Your time commitment can vary, and you’ll have the opportunity to specify a number of hours per week that you’re willing to commit when you make your mentees an offer of mentorship. Typical weekly commitments can vary, but usually fall in the 1 to 4 hour per week range.

Contact us. We can help you arrange a week-to-week version of the mentorship in which they pay you up front, since an income share agreement isn’t a good fit for someone who has a job already.

We often reach out to companies with data-sets and host a data competition with their data to our community of data scientists. The process and the added value is very simple: Give us your data and your problem and we turn it into a competition. We currently have a four-week schedule to do this. So, let’s get in touch at!

PhillyTalent is 100% free for companies looking to hire or host a data competition.


During the 100 Days Milestone you will work with your mentor and few other candidates on three to four data science competitions to practice as many topics as you can during this time. By the end, you’ve built a competitive portfolio on GitHub and have gained training on several key technology stacks such as AWS Lambda, various databases, various storage APIs and ML Methods. We have put some of our contents public on our YouTube channels. You’ll also be attending at least three monthly hands-on machine learning events and several weekly events with few other candidates. We’ve held over 20 events in the past two years.

Mentorship is an investment. The data competition is simply the technique to deliver applicants’ technical skills to the next level consistently. But, ultimately our goal is for you to land a job. This is what the final evaluation of this collaboration and mentorship is based on: whether or not our applicant got a job in the relevant field.  Our ISA makes that explicit by giving your mentor a stake in your success. It also gives you the chance to get expert technical and career help from industry professionals, even if you can’t afford to pay for this collaboration up-front.

Because you only pay if the mentorship works, our incentives and the mentor’s are aligned with yours. We all want the same thing: for you to succeed and get a high-paying job. That alignment is what results in a true, productive partnership.

We don’t require a degree or experience. Period. Anyone who can read should be able to read code. But writing codes and writing production code takes a lot more than just being able to read and understand it. Still, universities and Code Academies do a hell of a job training students and members to read and write code. Which would be your direct competition in the job market.   So we do expect you to have a fairly solid base of knowledge in data science. The mentorship is aimed at refining those skills for an easier transition into industry.

If you aren’t sure what your chances are, ask us! You can message our team on chat, or reach out by email at

Yes. And that is the great thing about PhillyTalent.

If you’re hired in a technical position within 6 months of your mentorship, you’ll repay a percentage of your first-year salary that you’ll agree on with your mentor in advance.

We charge a fix 5% rate, for up to 2 hours of your mentor’s time commitment per week. Besides taxes and other transaction fees, PhillyTalent doesn’t add any overhead to this amount. Our job placement services are completely free for you as candidates once you join.

Our mentors are data science and machine learning professionals who have established themselves in their industry. They’re employees and alumni from AI groups at Apple, Uber, Tesla, Amazon, and other similar companies. We screen all our mentors carefully to ensure quality. You’ll also get the chance to interview your mentor yourself before you commit.

The skills you’ll work on will vary depending on your current skill set and interests. Normally you’ll focus on industry best practices in deploying ML models to production, DevOps, writing clean code, and doing proper data engineering and data cleaning.

If you don’t land a job in data science or a related field within 24 months of completing the program, you won’t owe the mentor or PhillyTalent anything.

Your mentor will guide you through a full-stack machine learning project over a period of time that you’ll decide on together. This is usually anywhere from 4 to 16 weeks, depending on the project and your experience level.

The mentor will provide support via office hours, code review, and general advice/guidance. PhillyTalent will check in regularly to ensure things are running smoothly. You’ll have access to our Meetup events, Slack workspace with other mentors, mentees, and PhillyTalent admins, ensuring 24/7 support.

In most cases, your project will involve building a full-stack machine learning pipeline: that means data collection and cleaning; building and training a model; and deployment with a working front-end. However, if you prefer to use your time differently, for example by focusing 100% on interview prep, that may be fine too – ask your mentor when you interview them.

You’ll still be asked to repay the mentorship fee. We want to ensure that our mentors are incentivized to see you hired as soon as you start, regardless of context.

However, you can request to defer repayment once you’ve started a job if your mentor agrees. Many mentees who land internships find it useful to defer repayment until they’re hired in a full time position.

If you already have a job but you want to go through the mentorship program anyway, you’ll go through the same process with the exception that your offer would be different. We charge a small recurring fee on a week-to-week basis for the mentorship where you pay this fee up front. On top of that, any technical job you hold at the end of the mentorship after six month period will qualify for repayment. We exact amount of this repayment amount will be included in your offer once you matched with a mentor.

An offer of mentorship always includes the percentage of your first year’s salary of 5%. That percentage will get paid out by credit card in monthly installments over the course of your first 12 months of employment.

  Yes, if you are interested in the data science job market. After the trial period you will need to update your account by making the commitment payment.  You can join three monthly and over 8 weekly trainings remotely once you make the commitment payment for free. Once you finish the first two week trainings you will recieve the details of a fixed weekly payments for the next stages in the offer if you got matched with any of our mentors.

No. You don’t owe PhillyTalent, or any mentors anything until you accept an official mentorship offer.

The PhillyTalent Income Share Agreement (ISA) is an agreement between you and a mentor once you finish your onboarding training and have passed all the requirements. The mentor invests time and expertise along with you by guiding you through a data competition aimed at developing your industry-critical skills and creating a strong portfolio. In return for this collaboration and mentorship, you agree to pay your mentor a small percentage of your first-year salary after you land a full-time job in a relevant field over the next 12 months.

Mentees’ technical backgrounds can vary quite a bit, depending on whether they’re recent graduates, or experienced software engineers looking for a career change. We also have a lot of applicants with limited work authorization and tight deadlines to land their job offer in both US, UK and few other countries. However, there are a few baseline skills that we expect any mentee to have and we ensure they are tested during PhillyTalent on-boarding process: they should be comfortable with Python, as well as basic data manipulation and model building libraries like Pandas and scikit-learn. We ask every candidate to sign up for our weekly trainings fif they have been away from coding for sometime. We ensure these skills are in place before assigning you to any data competition with an expert. Exceptions could be made if, for example, you have a candidate who is very experienced in Java or R, or one who is targeting certain kinds of data analytics positions that aren’t as coding-heavy.

  Yes. That’s the key metric for us to improve. We’ll connect you to our network of  partners through data competitions, monthly events and weekly trainings and we provide services like interview prep and technical profile review that directly help you with you job search. We expect a minimum of two screen calls from every candidate once you start your work with a mentor.

We track the quality and number of your job applications. During the first six days one of our lead mentors will work on several key topics in your job search. Once you matched with a mentor and started building your portfolio we track our candidates performance through forms. We match these information with other public accounts from our candidates such as GitHub, LinkedIn, Kaggle and other platforms.

We understand that not all talents are created equal. And some may have less time available to hone a new skill set but might have more working experience which will help them to recieve more interviews. Some candidates are fast learners and some are big-picture leaners where they need more information but eventually could be more creative learners. That’s why we don’t charge by the hour and we don’t send our applicants tutorials after tutorials. Not every one has time to do new homeworks. We give you guidelines for situations that you are absolutely in the dark but we’ve found out most of the time that’s not necessary. The competitions have clear creteria, we expent our applicants to grow and improve with the rankings. We use GitHub we easily let us track their work to do the last “commit” and we do have weekly checklists once an applicant starts on a competition.

If you aren’t sure where to start that is why we have created an on-boarding stage which we call ModuleZero. We have daily checklists along with a controlled daily workflows and technlogy road maps that will help you start building and curating all your profiles. For all these, we have checklists and evaluation forms that a lead mentor will go over with you on a daily basis for two weeks.

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