Before “data science” glimmered, Khalifeh Al Jadda was wrangling web clusters in 2005. This interview unearths his journey from academia’s trenches to building cutting-edge AI at Google Ads and revolutionizing businesses through AI. Let’s get to know his innovative spirit, groundbreaking projects, and insights on the future of search and empowering small businesses. But this isn’t just Khalifeh’s story; it’s a treasure map for aspiring data pioneers. Buckle up for practical advice on navigating this ever-evolving field and discover in our recent Leading with Data session how one man’s passion revolutionizes business, one byte at a time.

You can listen to this episode of Leading with Data on popular platforms like Spotify, Google Podcasts, and Apple. Pick your favorite to enjoy the insightful content!

Key Insights from our Conversation with Khalifeh Al Jadda

• The transition from keyword-based to conversational search unlocks new possibilities in user intent understanding.
• Building a successful data science team involves balancing skill sets, continuous motivation through challenging problems, and coaching for growth.
• Lifelong learning and adaptability are crucial in the rapidly evolving field of data science.
• The future of search engines will likely involve a multi-search architecture to cater to different user queries.
• Real-time personalized recommendations using deep learning require innovative solutions for real-time inference at scale.

Join our upcoming Leading with Data sessions for insightful discussions with AI and Data Science leaders!

Now, let’s look at Khalifeh Al Jadda’s responses to the questions asked in the Leading with Data.

How did your journey in data science begin?

My journey with data science started before it was even called data science; it was known as data mining in 2005. I was doing my master’s degree in computer science, and data mining was a hot research topic. Despite the lack of abundant data and sophisticated tools, the field was ripe with creativity and potential. My master’s thesis focused on enhancing the quality of web clusters using implicit user feedback.

What was your transition into the industry like after academia?

After completing my Ph.D., where I worked on glycol-informatics and big data to scale up machine learning techniques, I joined CareerBuilder. It was the right time and place, with the company investing in data science. I was among the first two data scientists on semantic search and recommendation engines. Later, I moved to Home Depot to build their recommendation engine and oversee online data science solutions. Each step was about challenging myself and stepping out of my comfort zone.

How would you describe your approach to data science?

I would describe myself as creative, innovative, and always challenging the status quo. I don’t accept existing solutions as the best or final answer. I’m excited to wake up daily to tackle new, challenging problems that haven’t been solved optimally. AI has unlocked many opportunities, and it’s about thinking outside the box to see how AI can improve people’s lives and help businesses grow.

Can you share some impactful projects from your career?

One project I’m proud of is the graph-based recommendation engine at CareerBuilder, which was one of the first of its kind. At Home Depot, we worked on real-time personalized recommendations using deep learning, which was challenging due to the need for real-time inference at scale. Another significant project was the vector search for Home Depot, which involved generating embeddings and matching them in milliseconds.

What is your view on the future of search engines?

The future of search is conversational. With LLMs (Large Language Models), we can now unlock conversational search, where the search engine can have a back-and-forth conversation with the user to understand their intent more accurately. This will enable use cases like project shopping, where customers can describe their project and receive a list of all the items they need. I believe we’ll see a multi-search architecture with different engines for different types of queries.

How did you transition from an individual contributor to leading teams?

Building a team from scratch at Home Depot was about identifying the data types we worked with and finding people with the right skill sets. Keeping the team motivated involved challenging them with interesting problems and educating business partners on what data science can achieve. Coaching and helping team members become leaders was also a key part of the process.

What are you currently working on at Google?

At Google Ads, my team focuses on helping small and midsize businesses globally maximize their return on investment from Google Ads. We build models to identify customers with the highest potential to benefit from Google Ads and suggest the right products or offerings. In a way, we are revolutionizing businesses through AI and upcoming technologies. 

What advice would you give someone starting their career in data science?

Start as a generalist to gain experience across the data science spectrum before specializing. Keep learning continuously, as data science is a lifelong learning journey. Don’t get attached to a specific programming language as the industry evolves. Network with influencers and thought leaders, and dedicate daily time to stay updated on industry developments.

Summing Up

This interview takes you through the complex fabric of data science, following the incredible journey of an experienced professional who saw it grow from the nascent stages of data mining to the cutting edge of artificial intelligence-powered search engines and is now revolutionizing businesses through AI. Khalifeh provides insight into creating teams, inspiring them, and overcoming the difficulties of data science leadership as they go from being an individual contributor to a team leader. The expert’s current focus at Google Ads is on using cutting-edge models to enable businesses worldwide.

Stay tuned with us on Leading with Data for more engaging AI, data science, and Gen AI sessions.

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