DTM E9. Analytical Musings – David Zakkam, VP Analytics, Swiggy

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David, was the VP of analytics at the time of recording, at India's largest and highest-valued online food ordering and delivery platform Swiggy! An industry veteran of 20 years, David has held multiple leadership roles in analytics at Mu Sigma, Swiggy, Meta & now Uber. On the show David will be letting us know about his journey in analytics from a biomechanical engineer, how he faced & overcame leadership challenges, his intriguing ISHQ framework, what he looks for in data scientists, and what's in store in the future for AI in general. Of course, he makes this episode a pleasure to listen to with his distinctive style & fun facts. This is a show not to be missed! 

Original episode air date - December 2020. Listen to the episode here, 

or on the below platforms

Social Links:

David Zakkam – David Zakkam (LinkedIn), @DavidZakkam (Twitter) 

Pronojit Saha, DTM Podcast - pronojitsaha (LinkedIn), @pronojits (Twitter) 

Show Notes & Summary:

(1:02) From Biochemical Engineer to Analytics Leader: David shares his analytics journey and the decision-making process behind his career choices.

1. David graduated as a biochemical engineer in 2001 and initially desired a career in the software field but joined a startup focused on computer-based drug development due to the economic conditions at that time. 

2. After completing an MBA, he joined Intel, which involved more sales-related work rather than technical tasks. 

3. He spent ten and a half years at Mu Sigma, where he performed various analytics roles, including being an analyst, leading teams, and managing client accounts, eventually overseeing all regions in Europe. 

4. At the time of recording, he was heading the analytics department at Swiggy for the past two years, focusing on four main areas: consumer satisfaction, restaurant growth, driver cost management, and optimizing app usage. 

5. In a project for a search engine in 2010, he and his team developed a system using machine learning algorithms to identify issues in real-time, reducing issue identification and resolution time from days or weeks to just a few hours. 

(5:29) Handling challenges in leadership roles

1. Leadership in technology roles presents challenges as it involves managing people with different motivations and aligning them towards common goals. 

2. David, with a strong technical background, finds it easier to handle software aspects than the people-oriented aspects of leadership. 

3. David advises to possess a leadership trait of remaining calm and not getting stressed, which positively impacts decision-making and shields pressure from the team. 

(7:27) Swiggy's Data-Driven Revolution

1. Swiggy highly values analytics, data science, and data-driven decision-making, making it a digital-first and ML-first company. 

2. The challenge at Swiggy is not the lack of data-driven decisions, but rather the limited capacity to handle all the data-related questions and requests due to resource constraints. 

3. The team's responsibility is to prioritize and say no to certain requests in order to focus on more important tasks, understanding that saying no to something means saying yes to something else. 

4. Swiggy aims to democratize data science and machine learning within the company, allowing more individuals to become ML practitioners, even if it's for simpler tasks, and make data science accessible to a wider range of employees.

(9:37) ISHQ Framework: Driving Impact, Speed, Happiness, and Quality in Analytics! 

1. Swiggy’s analytics team consists of around 100 people, making it a medium-sized group. 

2. The core goals of David’s team are defined by the "ISHQ" framework: Impact, Speed, Happiness, and Quality. The focus is on creating more impact, increasing speed, improving analyst happiness, and maintaining high-quality work. 

3. Efforts are being made to build internal capabilities and automate certain tasks to make analysts work faster and more efficiently, while maintaining a strong emphasis on quality. 

4. The team tracks impact, speed, and happiness regularly, considering them as key performance indicators. 

5. David’s approach emphasizes the growth and satisfaction of analysts, not just their performance, recognizing the importance of their personal development. 

(11:59) Qualities that a data science candidate should possess

1. David looks for four main qualities in analysts: technical skills, business understanding, structured problem-solving, and soft skills. 

2. Technical skills include expertise in data manipulation, such as SQL, and a broad range of statistics abilities, including machine learning. 

3. Business skills involve structured problem-solving and the ability to think from a business perspective and apply technology effectively. 

4. Soft skills, including communication, negotiation, and influence, are important for success in the role. 

5. Learning and staying updated is crucial in the ever-changing analytics landscape, and the team ensures it through dedicated learning time, access to education platforms like LinkedIn Learning and Udemy, customized courses within Swiggy, peer and social learning, and encouraging a culture of improvement and growth. 

(19:05) Work Hard, Party Harder: The Vibrant Culture of Success at Swiggy

1. The company culture is centered around working hard and partying hard. 

2. The employees put in a lot of effort to support the business but are also encouraged to take time off and not get too stressed. 

3. The team organizes various fun activities such as creating their own band, hosting live shows, fashion shows, and competitions. 

4. During Diwali, the employees were sent a gift hamper containing personalized items like a photo of the employee, chocolates, environmentally-friendly pencils, a mask, and a coffee mug. 

5. The team adapted quickly to remote work during the lockdown and organized online informal connect sessions and gaming activities. 

(24:21) David’s career learnings: The Power of Individuality. 

1. It is important to treat each person differently based on their unique needs and motivations while ensuring fairness. 

2. Just as algorithms are designed for personalized recommendations, treating individuals as an audience of one is beneficial. 

3. Treating people fairly means recognizing and accommodating their individual differences and needs. 

(28:23) Embracing Work-Life Integration: Redefining Success and Personal Fulfillment

1. David believes in work-life integration rather than work-life balance, considering work and life as interconnected. 

2. The company encourages employees to prioritize personal needs without feeling guilty. 

(33:41) Democratizing AI: From Secretive Power to Essential Business Advantage

1. AI used to be conducted in secrecy by a few statisticians, but now it has become a widespread practice accessible to everyone. 

2. Previously, companies that implemented analytics had a competitive advantage, but now it has become essential for survival in the market. Companies without analytics struggle to succeed. 

3. Many businesses, even those seemingly unrelated to data, now rely heavily on data-driven decision-making. Data has become integral to their business models and removing it would cause their operations to collapse. 

4.  Algorithms have become more advanced and accurate, allowing for a broader range of applications and possibilities in the AI industry. The potential for leveraging AI has significantly expanded. 

(36:22) Believe, Work Hard, Achieve: David’s advice for his younger self

1. Watching F1 driver Hamilton recently reminded David of the importance of believing in oneself and working hard to achieve goals. 

2. He acknowledges that self-doubt can hinder progress and that they have personally experienced struggles during such times. 

3. He emphasizes that those who believe in their capabilities and put in the necessary effort can make their dreams a reality. 

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