How to get job referrals as a shy data scientist?
By: Ian Xiao
Tags: Careers, Data Science, Editors Pick, Office Hours, Machine Learning

To set the expectation, you must be aware and ready for the following:

Genuine curiosity. You must be curious about the other person — just like how you are curious about insights in data as a Data Scientist.

You must be curious about the other person — just like how you are curious about insights in data as a Data Scientist. Be patient. Building relationships takes time and interactions. Don’t expect to get referrals after the first interaction with the first person. Not every interaction leads to something — and it’s totally fine.

Building relationships takes time and interactions. Don’t expect to get referrals after the first interaction with the first person. Not every interaction leads to something — and it’s totally fine. Discipline. There are things you can’t control. You need the discipline to keep working on it. Good things will come.

So, how can you get referrals without asking for one? Let’s break it down by the steps. The lessons here mostly apply to build cold relationships (e.g. people not in your family and existing friends network).

Step 1. Be referrable

There is no way around this. You must invest time to develop the basic skillsets and “brand” so that people can feel confident in bringing you to their teams and networks. How can you be “referrable”? As aspiring Data Scientists, I think there are a few ways to stand out:

Be unique. Unlike a few years ago, there are a lot of Data Scientists out there today. Everyone knows how to build models (or so they say). What else can you bring to the table? (e.g. domain knowledge, special technical know-how, doing things others don't want to do, etc.)

Unlike a few years ago, there are a lot of Data Scientists out there today. Everyone knows how to build models (or so they say). What else can you bring to the table? (e.g. domain knowledge, special technical know-how, doing things others don't want to do, etc.) Have some passion projects. If you’re a new graduate, working on passion projects (not school assignments) is a good way to show your work and stand out without years of experience under your resume. The best thing is that you are the master of your work, you don’t need permission from anyone nor need an offer letter to get started.

If you’re a new graduate, working on passion projects (not school assignments) is a good way to show your work and stand out without years of experience under your resume. The best thing is that you are the master of your work, you don’t need permission from anyone nor need an offer letter to get started. If you can’t tell, show. If you’re an introvert, you may not be very vocal about what you can do (you should try to though). The best way is to show. Showing your work is the best way to develop credentials among strangers. So, have a good portfolio on your own website or Github. Let your work advocate for you. This is a standard practice in the design community and many tech companies are already asking for links to your Github.

Mini-Challenge #1: Take your most recent school or passion projects, clean up the code, write a simple 1-page summary that your grandma can understand, upload to Github or your personal website. Show it to 1–2 people every day for the next 1–2 weeks. Get feedback and ask them to share with their friends who might find it interesting and useful. If you have trouble getting started, check out my post on a 12-Hour Challenge and try it.

Step 2. Find the right people from the right companies

Who are the “right” people? If you have all the time and energy in the world, you should get to know every interesting individual. But, realistically, you are likely running against a hiring schedule or runway to the next job. So, my recommendation is to make connections in the following order:

Hiring managers (e.g. engineering manager, team lead, chief data scientist, etc.) — it’s their mandate to find and hire good people. But managers are usually busy juggling between their day job and hiring. So make your interaction impactful (see Step 3 & 4).

(e.g. engineering manager, team lead, chief data scientist, etc.) — it’s their mandate to find and hire good people. But managers are usually busy juggling between their day job and hiring. So make your interaction impactful (see Step 3 & 4). Entry-level (e.g. data scientist, analyst, etc.) — although they may not have the decision power, junior people can still make introductions to the hiring managers (and some will become managers one day).

(e.g. data scientist, analyst, etc.) — although they may not have the decision power, junior people can still make introductions to the hiring managers (and some will become managers one day). Executives (e.g. C-level, Partners, Senior / Vice President, etc.) — it will be great to get a top-down introduction from the CEO to the hiring team. It happens, but most executives are too busy and are not involved in the day-to-day hiring process.

What are the right companies? In my experience, it’s more effective to get referrals from people who work at start-ups and medium-sized companies. Big companies usually have standard campus hiring processes for entry-level positions and most people would refer their friends and acquaintances who are more experienced to non-entry level jobs.

You can take a look at this article where I breakdown the type of data science roles and companies.

Understand what Data Science is really about. Find the type of data science role that fits your profile and interest. Come up with a list of target companies from small to large, the job titles, and start connecting with people on LinkedIn based on the priorities I suggested in Step 2.

Mini-Challenge #2: Write down 5 companies you want to join (1–2 big companies, 2–3 medium-size companies, and 3–4 start-ups). Find 1–3 data science/analytics job postings at each of these companies. Find 1–2 manager level people at each of these companies on LinkedIn. Connect with the ones in the same city or went to the same school first, don’t connect with everyone all at once. Send them a good introduction message by following Step 3 below.

Step 3. Write a good introduction message

Not everyone will accept and respond to your LinkedIn invitation and messages. From my experience, it’s a 50–70% acceptance rate and a 20–30% response rate (e.g. to get 1 person to respond, you need to connect with at least 8-10 people).

You can’t really do anything about the acceptance rate, but you can try to improve the response rate depending on how you write your message.

In my experience, shorter messages with clear asks and options work better. As an introvert, I used to think such messages are rude and abrupt. But it’s more important to respect others’ time by letting them know how they can help — and make it easy.

To make it concrete, here is a bad example that landed in my inbox a few days ago.

A bad example of LinkedIn message, author’s work

I know the person is looking for help, but I didn’t know what and how I could help.

Overall, the goal of the introduction message should be letting the recipients know why and how they can help with the minimum amount of mental energy.

Here are a few principles I follow:

be explicit and specific to your ask (short summary and/or links)

propose a time that works for you, then let the recipient decide (it’s easier to just look up the calendar than having to send extra messages to figure a time that works mutually)

provide options (but not too many)

let people say no, but leave the door open for follow up

With these in mind, here is how I’d re-write the LinkedIn message:

“Hi Ian, thanks for accepting my invitation. I love your articles on Medium (😉, okay maybe not the wink). I am exploring Data Science opportunities and saw an opening at your company (include a link if available). You can check out my resume and work here (links to resume and portfolio). Are you free to have a quick 15-min chat at 3pm this or next Friday? If you think there is a better person I can speak with, it will be great if you can connect us.”

You can take elements from this message and adapt to your own use. There are many good email and message templates online. Stick to the principles and find your own style.

Step 4. Meet, but don’t ask for a referral

Once you have a meeting set up with the right person from the right company and team with the right introduction message, now comes the moment of truth: get the person to put you in the interview process.

Having to chat with strangers and asking for favours might be the most overwhelming process for introverted data scientists. Unfortunately, I can’t provide a script because the conversation can happen in many forms depending on the personality of the other person, the mood of everyone before and during the meeting, the context of the hiring process, etc.

The good news is, at this stage, the person you are about to meet knows your intention (e.g. get a job at his / her team or get connected to someone else), is in a position to influence and act, and is open to help or at least listen to what you can offer. Your chance of getting a “referral” is already much higher than randomly talking to someone or sending in resumes to a generic inbox.

To make the conversation less daunting and more effective, here are a set of mental and conversational tricks I found useful:

Don’t treat it as an interview. Because the person will be assessing if they should help and bring you in, the conversation is an interview — formally or not. Despite this, tell yourself that “this is just like a conversation with a friend I haven’t seen in ages and we are just catching up.” Saying this in my mind typically put me at ease and not coming across as too desperate.

Because the person will be assessing if they should help and bring you in, the conversation is an interview — formally or not. Despite this, tell yourself that “this is just like a conversation with a friend I haven’t seen in ages and we are just catching up.” Saying this in my mind typically put me at ease and not coming across as too desperate. Take a gentle lead. More often than not, the hiring manager also doesn’t know how to talk to you or is preoccupied with and distracted by his day job. There will be dead air and awkwardness. This kills me as an introvert. So, after saying thanks to taking the call and the time, I’d typically suggest “why don’t I tell you more about my data science experience and would love to know more about your team and what you are looking for in the position …” Be friendly, be calm, be specific, and have your stories ready. I haven't come across anyone who says no to this suggestion.

More often than not, the hiring manager also doesn’t know how to talk to you or is preoccupied with and distracted by his day job. There will be dead air and awkwardness. This kills me as an introvert. So, after saying thanks to taking the call and the time, I’d typically suggest “why don’t I tell you more about my data science experience and would love to know more about your team and what you are looking for in the position …” Be friendly, be calm, be specific, and have your stories ready. I haven't come across anyone who says no to this suggestion. Offer, don’t ask. Toward the end of the conversation, both of you should know if there is a fit. If you still want to proceed, here comes the moment of truth. Instead of asking “can you refer me?”, which may be abrupt and make it awkward for everyone, make two offers: your resume and things you can be helpful of. You can say this: “I’d love to explore further, can I share my resume and portfolio to your email? Please take a look and let me know if you see any fit. I can follow up in a week or two.”

Now you’ve done what you can. Let life take care of the rest.

During the meeting, it is important to be able to facilitate conversations and tell good stories about yourself. It is too risky and expensive to practice with hiring managers (you need to be ready when you show up to the meeting).

Mini-Challenge #3: Prepare how you’d like to explain your work, what is interesting about it, and why it matters. Rehearse by yourself. Bring up the stories casually during drinks or coffee with your close friends. Aim to have them ask questions and follow the flow of the conversation. Practice thinking and articulating on the spot, avoid pre-scripting everything.