Data Science, Machine Learning, and Artificial Intelligence intersect with a common denominator called “Data”.

The difference between ML and AI is that ML is about autonomous programming and learning, eventually enabling AI and this is made possible by algorithms developed by AI/ML engineers. These engineers can forecast consumer behavior with predictive analytics.

Simple examples include conversational chatbot, voice recognition technology, personalized product recommendations, financial advice, health care, ETA predictions, image recognition, etc.

ML engineers teach bots to analyze large sets of data, learn from data and generate accurate results. Skills required include statistics, probability, data modeling, mathematics, and natural language processing.

AI engineers build models that can emulate human intelligence by teaching systems the process of learning, reasoning, and self-correction. Skills required include programming, statistics, signal processing techniques & model evaluation

If you’re interested in an AI/ML engineer job, you should know that the initial filtering will be done either by a junior talent sourcer or by an ATS. Here are some tips on creating an impactful AI/ML resume, LinkedIn profile, and Cover Letter. ️

🤖Use a combination resume format that highlights your competencies as well as chronological work past.

🤖 Include all relevant keywords with a mix of technical as well as functional skills.

🤖Avoid very heavy technical language (maintain a bit), jargon, abbreviations & write in layman’s language.

🤖 ️Aim for a resume that first passes the ATS barrier and then impresses the recruiter with your accomplishments.

🤖Keep it short and simple, a one-pager will be great but do not max out 2 pages even if you have 10-15 years of experience

🤖 The summary should be outcome-oriented with key skills rather than including too many adjectives, for example:

A veteran artificial intelligence engineer with 10+ years of industry experience focused on developing 15+ machine learning solutions for 5 global clients in the banking sector.  Credited for developing a customer segmentation algorithm that boosted the client revenue by 20%. Skills include Predictive Modelling, Data Mining, and Quantitative Analysis.

🤖 ️Show what difference you made with your work by including statements such as:

-Developed a SAS program that automated linear regression model saving 22 manhours per month.

-Prepped data and built a marketing mix model that lifted ROI by 10 basis points

-Built an automated surge incentives model that increased driver availability during peak hours by 22%

🤖 List out the series of projects that you have worked on with outcomes. Don’t forget to quantify them. ️

🤖Show off your certifications, with completion date, expiry date & institute name. Add these certifications with an (R)mark.  ️

🤖 Match your Cover letter & LinkedIn profile with your Resume, ensure they speak the same language, but don’t copy-paste unless they are facts.

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