A “how to” series by data science leader Piyanka Jain, President and CEO of Aryng
Can you feel it? Can you feel the confidence and excitement that comes from being on the cusp of a life-changing move? You should. You’ve confirmed you have an aptitude for analytics, you’ve zeroed in on the data science jobs rights for you, candidly identified your skill gaps, and done the work to get the skills you need. Now, it’s time to land that job.
In this, our last installment in this series, I’ll show you how to reap the rewards of all your work with a new job in data science.
Step 4—Landing the Job
You’ve come this far, but that last step—beginning your new career—seems daunting. How do you get an interview? And, when you do get face time with the hiring manager, how do you close the deal?
There’s a formula for getting an interview, you know. It goes like this:
Right Job + Right Resume + Right Time & Place (aka Luck) = Interview
Granted, you can’t control luck. But you can and should grab control of what you can:
- Write the right resume
- Find and apply for the right job
Let’s tackle writing the resume first. You already know a resume should be concise, yet meaty, but what does that really mean?
Write a resume that gets noticed.
1. Tell the story of you—as an analyst. Leverage your experience, regardless of your former role, to paint a picture of your strengths as an analyst. Show your expertise driving ROI or your business knowledge. Your resume should scream “analyst”, not “developer-turned analyst”.
2. Show them the money. Working on projects is great; driving ROI or revenue is better. Write your resume to show the dollar impact of your work. “Conducted A/B testing on homepage, resulting in higher login” can’t hold a candle to “Drove incremental revenue of $5M by testing variants on login”. Estimate dollar impact, if needed, and describe it as potential revenue gain.
3. Create an 8-second resume. The hiring manager will spend about 8 seconds reading your resume before deciding whether to toss it. Make the most of that time!
a. Use a Request for Quote (RFQ) chart format, clearly demonstrating how your experience matches the job requirements.
b. Address each job requirement with examples of your interest, exposure or proficiency.
- Boldface words that highlight your story, such as “5 years work experience”, “Spreadsheets”, etc.
- Keep the resume to one or two pages. Start with a summary of important elements.
4. Choose your words carefully. Use active verbs and meaningful words that demonstrate your skills, show your passion for data science and highlight the impact of your work. Briefly describe an instance when you demonstrated superb problem-solving skills, rather than just saying you have them.
Always calculate the potential impact of a project before engaging in any assignment. Use the Sizing/Estimation technique from Aryng’s hands-on business analytics course.
Apply for the right job.
- Be focused. Target jobs you identified in the first blog in this series. If your interests have expanded, add them to your list as second-tier
- Internal transfer is best. If you’re currently employed, find your next job where you already work. Your inside knowledge of the products and company culture gives you a huge leg up and significantly increases your chance of success.
- Search the right job sites. Use well-established job portals with fresh job postings
- Use your contacts. If you have a friend working for a company that posts a job that interests you, send him/her your resume with the job ID/link so they can forward your resume directly to the recruiter or the hiring manager or get you an informational interview.
- Make direct contact with the recruiter or hiring manager. Some paid premium services enable communication with the recruiter or hiring manager for some job postings. Many posts include an email for resume submissions, which allows you to follow up on your submission later.
Ace the interview.
You have an interview! Great news! Now do your homework to bring home the prize.
- Research the company. Learn the organization’s business model, revenue stream, customer set, products, locations, size, etc. The more you understand the company, the better you can tailor your responses to their context.
- Practice your story. Know what to highlight and what to de-emphasize. Avoid rabbit holes. Answer simply and don’t overshare.
- Demonstrate problem-solving skills. Analysts are first and foremost problem solvers. Most good data science interviewers present the interviewee with a hypothetical or real problem to assess problem-solving skills.
- Demonstrate impact. Reinforce your resume by showing the impact of your work in the verbal commentary of your background.
- Prepare for the technical interview. If the job requires technical skills, be prepared to demonstrate your knowledge of a certain tool or an analytics methodology. This may include using a whiteboard to illustrate your answer (for example, to write a piece of code).
- Prepare your questions. An interview is a two-way conversation, so prepare a list of questions to ask the hiring manager. If you don’t fully understand their revenue model, ask about it. Understand your future role by asking the hiring manager and other team members.
Finally, give them a glimpse of who you are, as a person and prospective colleague.
- Don’t be afraid to be passionate. Articulate your passion during the interview. I can’t emphasize this enough: Passion for the job often counts for more than hard skills.
- Be respectful and be honest. Be punctual, fully prepared, dress appropriately, and show respect for the interviewer’s time.
- Show confidence but be humble. Confidence inspires. If you can show that you can do the job, you remove hesitation from the interviewer’s mind. Be honest about what you do or don’t know and be confident without appearing arrogant.
- Demonstrate good communication and listening skills. Good analysts are awesome listeners—ever-curious about the customers, products, usage, etc. Show your listening skills by letting people complete their sentences. Clarify by paraphrasing or asking questions before formulating your answer. Be brief and succinct.
Conclusion and follow up.
At the end of the interview, ask for the next step in the hiring process, whether anything additional is needed from you, and how you stand up against the other applicants. Follow up with thank you notes to show you enjoyed the interview process and explain why you would love to work there.
A data science career transition takes work and commitment, but it is very doable. Every day I work with men and women who’ve made their dream of a data science career come true—and you can, too. Realize your passion by following the steps outlined in this four-part series and that dream job will be yours!
The Aryng analytics career transition premium track includes assessment, training, mentoring, and real-time client project experience as well as career coaching services such as resume writing, and interview prep.
Learn More About a Career in Data Science:
A highly regarded industry thought-leader in data science, Piyanka Jain is a frequent keynote speaker on using data-driven decision-making for competitive advantage at both corporate leadership summits as well as business conferences.
To learn more about Piyanka: