The Two Sides of Getting a Job as a Data Scientist
Are you a Data Scientist looking for a Job? Are you a Recruiter looking for a Data Scientist? If you answered yes or NO to this questions you need to read this.
General interview tips
Before going to some advices for the interview, here’s a list of a “common” process when applying for a Data Science position:
- A phone call where they will ask you about you and your experience. This is the first phone screen.
- If everything goes well you’ll get a second call, this time maybe from some Data Scientist that work in the company. This is the second phone screening. They will ask you more about you, your experience and also some technical questions. This is more likely to see if the things you said in your resume are true.
- (Optional) Data science task. They’ll send you a dataset and ask you several questions to see your abilities as a data scientist. Be really clear here. Write good quality code.
Advices from Kyle Mckiou to write good quality code as a Data Scientist:
Writing quality code is critical for data scientists, especially in 2018 and beyond.
As data science practices mature, more companies will demand automation, reproducibility, scalability, portablitiy, and extensibility for data science projects. To make this a reality, you had better be ready to write quality code.
Here are 10 tips to get you started as a data scientist:
1. Refactor aggressively. Don’t tack new code onto the end of a script when you should refactor
2. Follow style standards, e.g. PEP-8 for Python
3. Code defensively — always think about what could go wrong!
4. Avoid globals and minimize the scope of variables
5. Take those scripts and turn them into programs — create organized systems
6. Always unit and integration test
7. Automate your tests
8. Create a rigorous review process
9. Follow the rigorous review process, even when you don’t want to!
10. Provide honest, critical feedback as a reviewer and be open to feedback when your code is being reviewed
- Whiteboard programming. This maybe the harderst and more intimidating part of any process. Programming in a blank space. Just you and a piece of paper. Practice this a lot. You don’t need to write the code here perfectly, they want to see you thinking and getting into the solution. Talk and describe your thinking process, don’t be there quite.
- (Optional) Day of coding in the company. This is the final task, is not that common, but is an invite for their company to be there for a full day, seeing what they do and solving some programming tasks.
Advices for the interview? Here are the ones I could find, they are great:
The Recruiter side
If you are a recruiter for Data Science positions, first see whom is Data Scientist. Not an easy question but here’s my short answer to that:
A Data Scientist is a person in charge of analyzing business problems and give a structured solution starting by converting this problem into a valid and complete question , then using programming and computational toolsdevelop codes that clean, prepare and analyze the data to then create modelsand answer the initial question.
What data science is not:
We are much more than this.
Why is Data Science important?
Data Science and Analytics exists because hidden in the data there are treasures waiting to be discovered.
The Ways a Data Scientist Can Add Value to Business:
This is an extract of an amazing article by Avantika Monnappa
1. Empowering management and officers to make better decisions
2. Directing the actions based on trends which in turn help in defining goals
3. Challenging the staff to adopt best practices and focus on issues that matter.
4. Identifying opportunities
5. Decision making with quantifiable, data-driven evidence.
6. Testing these decisions
7. Identification and refining of target audience
8. Recruiting the right talent for the organization
Do you always need a Data Scientist?
Actually no. I recommend that you read these articles on the subject,
From those, an important quote I can take is:
… leveraging a data science team appropriately requires a certain data maturity and infrastructure in place. You need some basic volume of events, and historical data for a data science team to provide meaningful insights on the future. Ideally your business operates on a model with low latency in signal and high signal to noise ratio.
Without these elements in place, you’ll have a sports car with no fuel. Ask yourself if more traditional roles like data analysts and business intelligence may suffice.
Remember this words: A bad data scientist is way worse than don’t have a data scientist at all.
There’s lot of people wanting a job in Data Science, most of them are really intelligent people, wanting to help and have a path in this area, but be careful before hiring one. I recommend that you search for data science descriptions in the best companies out there, learn about their process, and learn from them.
Also, is not true that they need a PhD to be the best data scientists. They need experience working with data and solving business questions using data science. Before asking for a PhD, ask for knowledge, projects they have worked on, open source projects they built or collaborate, Kaggle kernels they created, related job experience, how did they solve an specific problem.
Data science is not just an IT area, is IT+Business, you need to be sure that the data scientist you hire can adapt to the company, understand the business, have meetings with stakeholders and present their findings in a creative and simple way.
Read this blog post for more information:
A Field Guide to Recruiting Data Scientists
Every day humans collectively create the equivalent of 530,000,000 million digital songs or 250,000 Libraries of…blog.entelo.com
and from there some important tips to recruit data scientists:
- Recruiters, work closely with hiring managers to build out accurate job descriptions.
- Iron out nuances to distinguish which types of data scientists will be the best fit for the business’ needs. Hone in on the skillset and experience of the type of data scientist you’re looking for.
- Think long term. Understand how the org plans to leverage this role within the product roadmap.
- Set realistic expectations of available candidate pool. There are more roles than candidates, so recruit accordingly.
- Build a list of ideal candidates and calibrate with hiring manager to gauge fit against reality of talent market.
A good quote on the recruiting process from Vin Vashinta:
Aspiring data scientists want 1 thing from the companies that don’t hire them: an explanation. In many cases their only response is silence. How’s an aspiring data scientist supposed to know what to work on, if companies won’t tell them?
Aspiring data scientists aren’t psychics, but they are hardworking & willing to learn. They’ll rise to the challenge if companies start telling them where the bar is.
Peel back the hiring process at most companies & you’ll find they can’t objectively answer the question, “Why didn’t you interview or hire this person?” I teach clients how much they can learn by examining the candidates they reject as closely as they examine the people they hire.
There’s value to both candidates & employers in the answer to that question. Companies have an opportunity to improve their hiring process. Candidates get the opportunity to be better prepared for their next application with the company.
Beyond the value, it’s the decent thing to do for someone who took the time to apply. Hiring is all about making connections. Silence shows the company doesn’t care enough to treat people the right way. That’s something candidates remember.
I hope this post will help everyone in the Data Science world. Let’s join together and help each other transform the world into a better place. Remember to have fun and that there’s much more in life than work, I love what I do, but take time for your family and friends, be happy and be kind to one another.
For more information or if you have questions just add me and we’ll chat there:
Bio: Favio Vazquez is a physicist and computer engineer working on Data Science and Computational Cosmology. He has a passion for science, philosophy, programming, and music. Right now he is working on data science, machine learning and big data as the Principal Data Scientist at Oxxo. Also, he is the creator of Ciencia y Datos, a Data Science publication in Spanish. He loves new challenges, working with a good team and having interesting problems to solve. He is part of Apache Spark collaboration, helping in MLlib, Core and the Documentation. He loves applying his knowledge and expertise in science, data analysis, visualization, and automatic learning to help the world become a better place.
Original. Reposted with permission.
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