The Future in Tech

Applying for job opportunities goes beyond simply submitting applications through company websites or job boards. It involves reaching out directly to hiring managers and recruiters through cold emails or cold direct messages (DMs), presenting a personalized pitch and highlighting why you are a suitable candidate for the position. By proactively connecting with professionals on platforms like LinkedIn and requesting informational interviews, you can distinguish yourself from other applicants who rely solely on the applicant tracking system (ATS) screening process.

Nick shared his experience of landing a job at Google. Despite being an intern at Microsoft in Seattle while an opportunity with Google's Nest Labs arose in San Francisco, Nick decided to RSVP to the event and not attend physically, ensuring his resume would be on file.

A month later, Google contacted him, expressing interest and initiating the interview process. However, there was a period of silence, so Nick followed up with multiple emails. His persistence paid off when the recruiter scheduled his first interview. This experience taught Nick two valuable lessons: the importance of exploring alternative application channels and the significance of tenacity when advocating for oneself.

Some people believe that it is impossible to prepare for these interviews, assuming that anything can be asked, or that a high GPA or strong academic background is sufficient. Nick emphasizes that interviewing is a distinct skill, separate from academic performance or technical expertise.

For software engineering roles, interviews often focus on data structures and algorithms, and resources like the book Cracking the Coding Interview. Data science interviews tend to be more open-ended, making preparation more challenging due to the diverse range of topics involved, which is why Nick wrote his own book Ace the Data Science Interview. Nick dispels the notion that preparation is unnecessary, highlighting the existence of common patterns, strategies, and frameworks that can be applied to tackle the most frequently asked questions in these interviews.

He encourages exploring opportunities across the board without worrying about the company's size. Nick has enjoyed working in startups, experiencing roles in data product and evangelism, etc. While big companies offer attractive perks, the startup environment holds a special place in his heart, which is evident in his own venture, datalemur.com. The key to success lies in the effort put into the job and projects, along with dedicating additional time outside of work to continuous learning.

Join us as we explore the transformative power of data science and AI with Walter Shields, a renowned data expert, author, and educator, uncovering the history, innovations, and future implications of these fields.

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Cold emailing means reaching out to individuals you don't know. Ensure that your message is relevant, friendly, and demonstrates effort. Grab attention and establish a personal connection. Highlighting shared interests or achievements, you can capture their attention and increase the chances of forming a meaningful connection. Simply stating, "I want a job," is unlikely to stand out. You gotta show that you're response worthy.

Cold email tactics are not a guaranteed solution and won't yield positive responses every time. It's a numbers game, and rejection is common, even for someone with internships at Google and experience running a startup. However, even if the majority of people ignore your emails, if one out of ten responds positively, it can lead to significant opportunities, such as informational interviews or referrals.

Cold emailing is more effective when you have a solid foundation of portfolio projects and accomplishments to showcase. Reaching out without any prior work experience or projects is unlikely to yield results. Having a public presence, such as publishing articles on Medium, sharing code on GitHub, or creating popular data visualizations, increases the chances of getting noticed and receiving a positive response. Without these accomplishments, it's best not to solely rely on cold emails as a means of securing opportunities.

When sharing your work, it's crucial to ensure it is public and provides relevant information. Data analysts can make an impact by creating dashboards using tools like Tableau and making them public. Aligning portfolio projects with desired industries or personal passions demonstrates enthusiasm and commitment to potential employers or collaborators, making individuals stand out.

By connecting personal passions with projects, individuals can highlight their diverse interests and showcase their commitment to data and technology. Through discussing the features, algorithm, development using Spotify data, and passion for music and data, individuals can convey enthusiasm and engage others. Sharing personal stories, helps establish connections and likability, even if the projects are not directly related. Demonstrating passion sets individuals apart in the eyes of potential employers or collaborators, as it showcases a level of commitment beyond mere interest in data.

Maintain enthusiasm and avoid becoming jaded. Even a small amount of care can be a competitive advantage. Find ways to stay engaged and excited about the work and company vision. Embrace a flexible mindset, as today's job market allows for rapid job switches and title changes, especially in the data field. One's career can extend beyond the nine-to-five job, encompassing activities such as teaching courses, publishing books, hosting podcasts, contributing to open-source projects, and offering consulting services. The modern world's opportunities, fueled by social media and blogging, enable individuals to pursue multiple avenues of income alongside their full-time jobs.

Undertake side projects related to one's job. While good managers may grant some time for such endeavors, often individuals need to find extra time to pursue them. Demonstrating ideas is crucial for gaining support and recognition. Additionally, taking on long-term projects and maintaining an attitude of experimentation and adaptability brings excitement and fosters progress.

Transitioning from engineering at Facebook to becoming a content creator and data expert, the speaker shares how sharing career advice on LinkedIn gradually built a substantial following. With 50,000 followers, they gained the confidence to write a book. Similarly, engaging in small initiatives that gradually snowball can lead to unforeseen opportunities. Visualizing the potential and taking small steps consistently helps build confidence, not just for others but also for oneself.

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July 10, 2023

In this episode, I talked to Walter Shields, an influential data science expert and passionate teacher. Walter shares his journey in the field, the rising role of Artificial Intelligence, the concept of 'Dark Data', and the evolving landscape of technology in media. His unique insights provide listeners with an enriching perspective on the current state and future direction of data, AI, and media.

Walter delved into his personal background (00:59) and the series of events that led him to immerse himself in the world of data and databases. As he found his feet in this new field, he discovered a strong passion for teaching and sharing his newly acquired knowledge, which manifested in a Meetup group (02:38). The surprising success of his Meetup group drew a large audience, affirming his belief in the widespread interest in data learning (07:29).

He expressed his deep-seated desire to make learning about data accessible and interesting. Walter placed a strong emphasis on the qualities of a good teacher, particularly the ability to approach topics from various perspectives (14:49). As he shared his journey, he shed light on his fascination with SQL and its limitless possibilities (16:27).

Explore the transformative impact of AI and data in advertising and media as we talk with Danny Ma, Principal AI Engineer at Lumos, who shares his expertise in data science and its applications in the industry.

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Walter explored the profound effect of AI on the data realm starting at 17:15. He discussed the industry's evolution from traditional data analytics to more advanced data science techniques, highlighting the importance of domain expertise (20:30).

Addressing the surge in data volume and its related challenges (21:41), Walter turned to the transformative potential of AI in hiring processes (23:25). He contemplated AI's capacity to eliminate biases, thereby fostering diversity in the workplace (24:58).

The dialogue moved to the interplay of technology and media at 29:39. We reflected on how technology has revolutionized news and radio, touching on the democratization of media and the role of younger generations (31:57).

He pointed out that technology's impact goes beyond media consumption, creating opportunities for innovative content creation and distribution (34:06). Amid advancements, Walter emphasized the need for accountability and a comprehensive AI education (28:25).

Walter introduced the concept of dark data, marking a new phase in data exploration (39:13).

With AI's continued development, he suggested that the data science field would undergo significant transformations (44:28). Beyond data, Walter discussed AI's impact on life-changing scenarios, its reach into social media, and its role in sentiment analysis (47:49). He further highlighted AI's significance in tailoring marketing campaigns and its relevancy across diverse industries (52:11).

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Data has been at the forefront of every breakthrough in technology, so it's no surprise that in the Age of AI, data has played a key role, helping the algorithms train and learn to think like humans. In this episode, we explore the role of data in AI with someone with a background in research, neurology and learning. Sadie St. Lawrence is the Founder and CEO of Women in Data, with a mission to increase diversity in data careers through awareness, education, and empowerment. Sadie worked in a neuroscience lab studying emotional learning and memory. She has been working in data science and AI for the past ten years. We'll explore why the AI algorithms are able to behave like humans, why they hallucinate and if it's even possible for them to reach the depths of an AGI (Artificial General Intelligence).

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