There are several questions that job-seekers frequently fail to answer in their resumes. So, we have created the Resume FAQs to answer all your questions to do with resumes.
1. What is the best recommended outline or section for an effective Data Scientist resume from INSOFE’s point of view?
A. Professional Summary/Objective
- Confident Data Science intern with a commitment to excellence and hands-on experience in NLP and text mining.
- Data Scientist with a strong math background and 2 years of experience using predictive modelling, data processing, and data mining algorithms to solve challenging business problems.
- Senior Data Scientist with 3+ years of hands-on experience leveraging machine learning models and data mining to uncover insights.
B. Education (only if you are a complete fresher- else you can add this section after Hackathons)
C. Certifications (INSOFE PGP can be included here)
D. Work Exp – Examples
- Applied decision tree analysis, using R to predict whether an email is a spam
- Created customized reports in Tableau for data visualization
- Created machine learning models with Python and scikit-learn to predict energy usage of commercial buildings with 98% accuracy
E. Projects (INSOFE internship can be included here)
F. Hackathons (MiTH can be included here)
G. Skills (only technical skills should be listed) Examples:
- Data Processing
- Programming in R and Python
- Performance Optimization
- Data Collection and Analysis
2. What is an Objective/Professional Summary? Why is it important?
There are two schools of thought on whether you should include an objective statement in a resume or not. Some career counsellors support the use of career objectives/professional summary while others are against the same, stating that it takes away space from the more important things like skills, projects, experience, etc.
If your resume objective is poorly constructed or looks like every other, you can easily distract the attention of the employer. On the other hand, not using an objective statement on your resume gives employers the idea that you are not sure about what you are looking for.
This 4 to 6-line section of professional summary provides a recruiter a snapshot of your skills, experience, and achievements. It gives them a glimpse into what you’ll bring to the table if you are hired and – when done well – entices them to continue reading your resume. Professional Summary or resume objective acts as the pitch of your resume. It mentions the goal and objective of your career. Even though it is not a strict requirement to include a resume objective in your resume, a well-written objective can help you catch the attention of the recruiter.
A decent data science resume should tell a story within 20 seconds that is targeted towards the job description and the organization hiring for the role.
For example, a candidate applying for a software development data science job role, the story can be something like –
“I understand big data technology like Apache Hadoop for data processing. I have hands-on working experience in developing accurate and fast machine learning models.”
If you are applying for a business focussed role, then your data science resume should convey a story like-
“I have hands-on working experience in machine learning and statistics to draw meaningful insights from big data. I am good at communication and storytelling with data.”
3. How do I write my projects to get more visibility?
If you have some relevant experience to showcase your skills then listing a lot of independent projects is less critical. However, if you do not have work experience and you are fresher, showcasing your proactivity, and technical pro-points via project work is highly helpful or rather a necessity.
From the INSOFE point of view -One should not add project details under work experience as it is important to keep details of work experience concise to give a quick overview. For any project that is important for you to highlight, split them as below; Don’t list common projects or homework. They aren’t that helpful in distinguishing you from other applicants. List projects that are novel.
A. Academic Projects
- Projects done during education, including while at INSOFE.
- An internship would be a great project.
- PhD but not MiTH (PHD is over 2 weeks with some elements of projects. MiTH is primarily hackathon).
B. Industry Projects
- Projects done during any work experience.
- If you were part of a specific consulting or research team internship with a partner company at INSOFE, then you can mention it here.
C. Self-Driven Projects
- Projects which you did on your own with no specific guidance other than web resources.
- Likely to have less impact since there is no way to corroborate it.
- Show results and include links to tangible evidence like a white paper, blog or a working demo. If you participated in Kaggle competition, put percentile rank as it helps the person reading your resume understand where you compete.
A project without any date/affiliation/evidence is useless information. Mention date and affiliation everywhere. If you have done a lot of Kaggle competitions and performed decently, you should put a link to your Kaggle profile at the end of the projects section with a quick comment on the range of competitions/your performance, as this will be a further good proof-point of your competence level.
4. How does adding hackathons and programming competitions on a resume boost my employability?
Hackathon experience can pay off in your favour while writing a resume. If you are currently employed, it shows you are passionate about what you do and are continually looking for new and different challenges to put up against your knowledge base. For those who are unemployed, Hackathons are critical ways to keep your skills fresh and even add to your portfolio. It’s a good way to boost the appearance of your work history during any employment gaps.
From the INSOFE point of view, Kaggle or any other open Hackathons participated in can be added in your resume with some evidence like the duration of the hackathon or dates, including links of hackathon if possible like GitHub, etc.
5. How to write work experience in my resume in the most effective/impactful way?
Tailor your experience towards the job as it is the core of your resume. But if you don’t have work experience – Focus your resume on Hackathons or independent projects, like capstone projects, independent research, thesis work, or Kaggle competitions.
Let your experience come out clearly & quickly. No One should have to scroll or search to find your total work experience. Do not include granular details of your work experience. Only a quick summary should suffice.
6. What skill sets should I mention for my resume to get selected?
A Data scientist position requires a unique set of skills that lets you ingest, transform, visualize and model datasets. You also need to communicate constantly with diverse stakeholder groups. So you’ll need to show a combination of technical skills and soft skills to make an impression.
Do list technical skills that the job description mentions. Adding resume elements that emphasize what’s mentioned in the job description is a subtle but powerful way to make your resume stand out.
Don’t give numerical ratings for your skills. If you want to rate yourself on your skills, use words like proficient or familiar or things like that. You can even exclude assessments altogether.
To Know the Ten Commandments of Resume Building, visit our blog here
- Naukri’s Official Blog