Creating A Data Science Resume

Guide to design a stellar resume- the crucial first step to approach a Recruiter or a Hiring Manager for the job you want.

We all know why we need to create a resume in the first place, right? A resume reflects one’s experiences, qualifications and most importantly, how well one will fit into a role. This blog post will cover the key points one should keep in mind while creating a resume.

Data Science is an industry of various opportunities. We are creating data with every click, every swipe, every search, every shopping trip, every stream. So, needless to say, it is an industry of rising demands. Data Science has seen about a 45% increase in the Indian market’s jobs in recent years, a study shows.

To keep up with this demand, companies are hiring for the DS role from all over the place. Even during the campus placements, the companies get hundreds of resume for two or three positions.

Now, let us think about a recruiter, Twyla Sands. She is getting hundreds of resumes every other day from candidates who want to apply for a job. She has responsibilities other than selecting resumes from this huge pile. She cannot spend more than 30 seconds looking at a single resume. Thirty seconds are all one has to hold Twyla’s attention.

We all know the gender gap is still prevalent in every industry, and our tech world is no exception. There can be several reasons for this gap to be in place, but I will quickly talk about two research studies.

Use the active voice. A resume is all about putting your experiences and achievements in one place. It should be all about you, a definitive subject who’s performing the action.

A research study by Kieran Snyder has suggested that creating a resume could one contributing factor. The study has looked into 1100 resumes (50% men-written and other half women-written resumes). Snyder found out that there is a stark difference in styles of resumes between genders. The differences are- length, organisation of the sub-sections, way of writing work details and relevant achievements and usage of verb-heavy action words, just to name a few.

A social psychologist at Skidmore College named Corrine Moss-Racusin conducted another research on gender bias in STEM faculty. The experiment was to give scientists two identical resumes of candidates to evaluate (similar in terms of qualifications and experiences). The only difference between those two resumes was one was had the name John and another one Jennifer. Racusin and her colleagues asked over a hundred STEM professors to assess these two resumes for one job role. Racusin presented the outcomes at the Stanford University School of Medicines, and the result was startling. Despite having similar qualifications and experiences, professors found Jennifer’s resume to be significantly less competent than John’s. So, there is a long way to eliminate these implicit biases present in people’s minds. But what we can do is to strive to design a more robust resume to reduce these gaps.

Think of it like your model tuning exercise.

With that in mind, let us dive right into the following key points to create a strong resume.

1. Template

Choose a template which is visually appealing and professional.
The length of the resume should not be more than two pages. Remember Twyla, our recruiter. It would be convenient to put all the relevant information she needs to look at a resume concisely in a single page.
Be consistent with your font style and size. ‘Comic Sans MS’ is not a suitable font style while creating a resume.
Save your resume in PDF format unless otherwise specified.

2. Personal Information

Put your name, phone number, email id.
Preferably put this section on top of your resume.
There is no need to put your full physical address or your parent’s names (Yes, I have seen people do this), etc.
You do not want to put an email address like ‘[email protected]’. A professional email id could be ‘[email protected]’ or ‘[email protected]’.
Phone number should be in a working condition. Remember, Twyla should be able to reach you while calling.
Put the link for your LinkedIn profile. If you have a Medium blog, GitHub or Kaggle profile, feel free to put those links as well. Do not hesitate to showcase your work as long as it is relevant.
Make sure the links are clickable in the PDF versions of your resume.
If you decide to put in your website/LinkedIn/Github profile, do not forget to succinctly curate these profiles. Add descriptions about your projects, courses, additional work etc. Which one do you think would be more appealing to Twyla; an empty profile with just headers or a well-written profile?

3. Summary

Add an executive summary to begin your resume.
It should not exceed more than 3–5 lines.
Add relevant work experience.
Add a few points about your skills, passion for DS, and the reason you want to work in this company.
If you are about to embark on your data science journey, write about your learnings and how well you think to fit in this role.

Let us see two examples of writing a crisp summary. A fresher can write, ‘Junior Data Scientist with Post-Graduate degree in Analytics and Machine Learning from IIM-B, skilled in Statistical Models, ML algorithms and programming. Experience in project-work for different real-life problems, especially in the finance industry.’ Another example could be, ‘Data Scientist with six years of work experience in the retail domain, focusing on statistical modelling, machine learning techniques. Achievements include successfully delivering a multi-staged forecasting system for workforce management. Completed Masters Degree from IIT Bombay in Applied Statistics.’

4. Experience

Start with your most recent experience.
List only relevant experiences.
Use the active voice. A resume is all about putting your experiences and achievements in one place. It should be all about you, a definitive subject who’s performing the action. Snyder’s study found out that men tend to write more verb-heavy action words instead of collaborative languages. Usage of passive voice means the subject is acted upon by someone else.
Present some measurable accomplishments (if possible). We can write ‘achieved 85% accuracy after tuning the model parameters’ or ‘increased profit margin by 30% by successfully tuning model parameters.’ Twyla might not be from Stat/Economics/Maths background to understand the implications of achieving 85% model accuracy. She understands the company business, and ‘increasing profit margin by 30%’ is much more engaging than ‘obtaining 85% accuracy’.
Use 3–5 bullet points.
Add company name, designation and years you worked with them.
For freshers, you can describe your class projects, dissertations or any additional or freelance work, if any. Try to include the objective, techniques used and measurable achievements.

Let us see one example-

Led a team of Data Scientists to build a forecasting solution using an ensemble of Machine Learning approaches.
Optimized CRM database for a high-volume real estate firm.
Decreased wasted phone and email time by 57%.
Used matplotlib to create real-time ROI graphs that helped the teams focus on high-profit business.
Gained 20% increase in annual profit.

While writing down the Skills section, always remember to look at the JD and include the relevant skills.

5. Skills

Why is this section so important? First, collating the skills relevant to the job description you are applying will help Twyla understand your resume, and as a result, you better. Second, companies often use application tracking software to parse incoming resumes. The software will give the matching percentage between the job description posted by the company and your resume. Lower the score, higher the possibility of turning that up in the reject pile. While writing down this section, always remember to look at the JD and include the relevant skills. You can break this into multiple sub-sections as well,

Technical skills- statistical analysis, hypothesis testing, regression, time series forecasting, machine learning algorithms etc.
Programming languages- R, Python, SQL etc.
Soft skills- presentation skills, visualisation, problem-solving skills etc.
Working experience with platforms- Google Cloud Platform, Azure etc.
Experience with different tools- Tableau, Git, MS Excel etc.

The JDs often contain package or library names such as dplyr, forecast for R, scikit-learn, and pandas for Python. In that case, add the packages or libraries you know.

6. Other projects/publications

Write other relevant projects if you have. E.g., Kaggle competitions if you have participated in any.
Describe if you have any published papers/talks (existing or upcoming). Add the link to the publications.

7. Education

Add college name, location, the degree you obtained
If you are starting your Data Science journey, you can write down some of the relevant courses you have taken.
Additional relevant course, certifications (MOOCs) if you attended any.

8. Other activities

Add any additional activities or initiatives that you are actively part of (for example, Placement coordinator in your college, part of the interviewing team in your present company)
Any award (relevant) that you have received.

There is no hard and fast rule in which order these sub-sections should go in a Resume. Depending on the job description, you might need to change the ordering a bit. If you are a fresher, you might want to emphasise the education and skills section more than the experience section. If you are applying for a job in Finance domain and have only worked in tech or retail field, try to bring out some of your skillsets, which can be useful in the finance sector. Consider it like your model tuning exercise. It is impossible to know everything in Data Science. Remember to be honest, concise, confident and do not hesitate to put your work upfront.