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How To Get A Data Science Job At LinkedIn

How To Get A Data Science Job At LinkedIn


With such a rich member data, LinkedIn can provide aspiring data scientists with an opportunity to explore and apply their knowledge like few others can. Collaborating with product managers, engineers, and others, the company’s data science team can leverage billions of data points to help drive data-driven business decisions for the company.

Susan Mathew, Head HR at LinkedIn India, has been associated with the company for over half a decade now. This has made her privy to the changes in the hiring landscape that came with the emergence of new tech roles in the last few years.

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And this includes a cross-functional data science team that has been creating a huge impact by helping discover new opportunities and gaining valuable insight for the company.

“The data science team at LinkedIn tackles challenges across product, sales, marketing, and more,” says Mathew. “They use insights from data to drive strategy, identify business opportunities, apply statistical inference to optimise, and build engineering solutions to realise the company’s long-term vision,” she adds.

Hiring Process For Data Scientists At LinkedIn

While data science is booming in India, and many institutes are emerging as great sources for this type of talent, Mathew feels that it is still a challenge to find the right mix of skill sets to suit the company’s needs at a given time.

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But what does the average hiring process at LinkedIn look like for a candidate applying for data science positions?

“As a first step, we usually conduct a preliminary telephonic screening,” says Mathew. “Should the candidate get through, they may be invited for in-person discussions with project leads. These discussions last anywhere between 45-60 minutes,” she added.

ALSO READ: The Dos And Don’ts Of Hiring Data Scientists

The company has an ongoing Talent Attraction, Branding, and Engagement strategy where it also identifies and engages talent through data tech talks, meetups as well as conferences. 

Moreover, according to Mathew, interviews for data scientists at LinkedIn India are competency-based. “Focus is on aspects like data manipulation, algorithmic coding, statistics, A/B testing and problem-solving,” she adds.

Required Skill Sets

As part of the company’s diversity initiative, LinkedIn hires candidates based on skill sets alone, and not necessarily based on their industry, domain or educational background.

However, preference is for someone with a degree in a quantitative discipline, which includes statistics, applied mathematics, operations, research, computer science, engineering and economics, or related practical experience.

ALSO READ: Five Non-Traditional Ways Of Hiring Data Scientists In India

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So what are the skill sets they look for?

“We expect candidates to have experience working with large amounts of data with SQL (Teradata, Oracle, or MySQL) and R, or other statistical packages,” says Mathew. “We also look for experience in a Unix/Linux environment for automating processes with shell scripting, as well as programming in Python, Java, or another language,” she adds.

What Do Data Scientists Do At LinkedIn?

According to a data scientist at LinkedIn, his team is responsible for a few tasks that stretch across disciplines. Largely, they develop an in-depth analysis of both structured as well as unstructured data and create machine learning models to drive value for the company in the long-term.

Additionally, they also collaborate with business partners to design core metrics and develop valuable business insights using automated dashboards and data visualisations to track those metrics.

This data scientist has enclosed some sample interview questions for aspirants applying at LinkedIn:

  • What would you say is your favourite kernel function?
  • Visualise a dataset of page views. If each row represents a one-page view, how would you differentiate between scrapers and real people?
  • If a company is unable to carry out an A/B test on a feature before launching it due to engineering constraints, how would you determine how a feature is performing?

“Our talented technical team has created and maintained our platform, tools, and features, building the company into what it is today,” says Mathew. “Our mission is to push the boundaries on what we can do with our data and maximise the power of data to benefit all of LinkedIn,” she adds.

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