What I *actually* do as a Data Scientist in 2024 (everything you need to know)

Summary

Viven, a data scientist with nearly 8 years of experience in Sydney, Australia, provides an in-depth look at the daily responsibilities and evolving role of a data scientist in 2024. She discusses the impact of company size on job scope, the importance of data analysis for business decisions, and the misconception that AI will replace data scientists. Viven emphasizes the ongoing demand for data scientists across various industries and the importance of staying updated with technologies like AI and prompt engineering for career longevity.

Takeaways

• 🔍 The role of a data scientist varies depending on the industry and company size, with larger organizations having more structured and siloed teams, while startups may require a more diverse range of responsibilities.
• 💰 Data scientists are in high demand and can expect competitive salaries, with an average starting salary in Australia being around $130,000 to $150,000 per year for those with 2-3 years of experience.
• 🛠 A data scientist's day-to-day tasks include extracting and analyzing data, building predictive models, and helping stakeholders make data-driven decisions.
• 🧩 Working at a startup allows for more direct impact and end-to-end project involvement but may come with less structure and fewer resources compared to larger organizations.
• 📈 Data scientists use tools like SQL for data manipulation, and programming languages such as R and Python for building machine learning models.
• 🚀 The role of a data scientist is intellectually stimulating and offers the opportunity to drive business impact through data analysis and model development.
• 🛑 Despite the buzz around AI and machine learning, a significant portion of a data scientist's job involves data cleaning and handling ad hoc requests, which can be frustrating.
• 🌐 Data scientists need strong attention to detail, as their work involves coding, data delivery, and model building that must be reliable and accurate.
• 🗣️ Communication and influence are key skills for data scientists, as they must effectively convey the business value of their work to non-technical stakeholders.
• 🔄 The field of data science is evolving with the rise of AI and low-code tools, but this does not mean data scientists will be replaced; instead, their role will adapt and require staying updated with new technologies.
• 🌟 To excel as a data scientist, one must be business-focused, understand the impact of their work on business goals, and proactively keep up with changes in the data space.

Q & A

What is the primary role of a data scientist according to Vivien's explanation?

A data scientist's primary role is to extract and analyze data to identify insights for stakeholders and use historical data to build models that predict future trends, ultimately helping businesses make better data-driven decisions.

How does the role of a data scientist differ between large organizations and startups?

In large organizations, the role is more structured and siloed, with dedicated teams for data engineering, analytics, and science. In startups, the role can be less structured, with the data scientist often wearing multiple hats and handling a diverse range of tasks across the data spectrum.

What is an example of a machine learning application in a dating app as described in the script?

Data scientists have developed algorithms for dating apps like Hinge or Bumble that prioritize the probability of two people matching by learning the user's preferences over time and showcasing more people with similar interests.

What percentage of Vivien's time is spent on product development in her current role?

Vivien spends 70% of her time on product development, which includes building machine learning models, working with engineering teams, and scoping out new features.

How does Vivien's role involve working with generative AI tools?

Vivien and her team have been building out tools and products that utilize generative AI to streamline processes and generate more insights for clients, focusing on prompt engineering and the integration of these tools with their data.

What are some of the technical tools Vivien uses in her daily work?

Vivien uses SQL for data extraction and manipulation, R and Python for building machine learning models, and tools like DataGrip, MySQL Workbench, and Google Sheets or Excel for various data-related tasks.

What are some of the harsh realities of working as a data scientist that Vivien mentions?

Some harsh realities include spending more time on data cleaning than expected, dealing with stakeholders who may not value the data scientist's time, and receiving ad hoc urgent requests that can derail other responsibilities.

What is the average salary range for a data scientist with 2 to 3 years of experience in Australia, according to Vivien?

The average salary range for a data scientist with 2 to 3 years of experience in Australia is between $130,000 to $150,000 AUD pre-tax.

How does Vivien view the impact of AI and low-code tools on the demand for data scientists?

Vivien believes that AI and low-code tools will not replace data scientists but will evolve their roles. She emphasizes the importance of understanding and integrating these tools into platforms and communicating their value to stakeholders.

What are some key skills that differentiate a great data scientist from a good one, as per Vivien's perspective?

Key skills that differentiate a great data scientist include attention to detail, the ability to communicate and influence, being business-focused, and proactively keeping up with changes in the data space, such as adopting AI tools and learning about prompt engineering.