Thread by @v_vashishta

Data Scientists looking for a new role and Recruiters looking for candidates speak 2 different languages. Miscommunication is the most common reason candidates disengage, drop out of the interview process, and reject offers. Why?
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Candidates eventually find out the role isn’t what they expected and there's not way to keep them involved in the process after that.
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Explaining a role to a Machine Learning Engineer vs. Data Engineer vs. Applied Researcher vs. Generalist Data Scientist vs. Data Analyst are all different conversations.
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I feel recruiters’ pain because each of those roles can require Python, but each role uses Python differently. Here are some steps to make the communications process easier.
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At some companies, everyone is a Data Scientist or Machine Learning Engineer. Other companies have crossover roles, so a Machine Learning Engineer might do some Data Engineering work too. Titles are inconsistent, so don’t 100% rely on them.
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Read the job description or resume and recategorize based on the actual role. Here’s how:

Pull back from the individual skills and create a list of capabilities.

Next, pull out the role’s workflow.
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Finally, tie each skill to a capability and the workflow steps the candidate will be using them in. The connection may not be specifically called out in the job description, so fall back on a generic, role specific workflow if necessary.
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The industry and area of focus within the company are essential. Those will change the context of the workflow, capabilities, and skills conversation.
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Lastly, pick out the most exciting work. Put yourself in the Data Scientists’ chair and find the projects or problems that are the most interesting.
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This process gives recruiters a framework to discuss the role using language that Data Scientists respond to. They want to work with recruiters who can discuss the role’s details in terms they understand.
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This all leads to higher offer acceptance rates and better candidate experience.
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I help businesses improve job descriptions and attract higher-quality candidates. A well written job description is a sign of a mature data science team, the kind top talent wants to work with. Schedule time here:
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