Demystifying Data/ML Careers

Your Guide to Data Analyst vs Scientist vs Data Engineer vs ML Engineer
April 25, 2024
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In the ever-evolving landscape of technology, data science has emerged as a dynamic and sought-after field, offering promising career opportunities for individuals with a diverse range of backgrounds and skills. To shed light on the journey into data science and provide valuable insights for aspiring professionals, we sat down with a seasoned industry expert, Susan Shu Chang, whose experiences and perspectives offer a glimpse into the multifaceted world of data.

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Q: Building on your earlier studies in Economics, can you walk us through how you discovered and transitioned into the field of Data Science?

In my journey, the path to data science wasn't a clear-cut one from the beginning. Back in my undergraduate years, I pursued economics with only a vague notion of what lay ahead after graduation. Economics appealed to me, and I performed well in it. Concurrently, I was diving into self-learning programming, primarily driven by a passion for creating video games. This self-taught journey included Python, a language I found versatile and exciting.

One pivotal moment came when a friend, who shared an economics background like mine, ventured into the world of data science and mentioned how our statistical knowledge from economics could mesh well with programming skills for data science. That conversation ignited my curiosity, leading me to delve into what data science truly entailed. Googling “data science” opened my eyes to a field where I could merge my interests and educational background.

However, breaking into data science required additional self-learning, especially in areas like Python libraries like Pandas, which were integral to this new domain but unfamiliar to me. Nevertheless, the foundation laid by my curiosity and success in statistical courses during my economics studies, and self-learned game programming combined into my initial data science skills.

Q: For someone considering a career in data, can you explain the key differences between a Data Scientist, a Data Engineer, a Data Analyst, and a Machine Learning Engineer?

Data roles encompass a spectrum of responsibilities, each with a unique focus.

Data Engineers form the backbone, designing and maintaining data pipelines and ensuring seamless data flow for downstream use. Their domain includes data warehousing and automation, often involving SQL and other pipeline tools like DBT or BigQuery. See section A in the graph below.

Source: Machine Learning Interviews by Susan Shu Chang

Data Scientists and Machine Learning Engineers take the baton from Data Engineers, specializing in leveraging data for insights and predictions. This would be the sections B in the graph above. Machine Learning Engineers and MLOps engineers specialize in the platform, encompassing section C in the graph.

Data Scientists explore data, build models, and extract insights, while Machine Learning Engineers focus on deploying and scaling machine learning models into production environments.

Data Analysts, on the other hand, are storytellers of data. They distill trends and patterns into actionable insights through reports and visualizations, often working closely with both data and business stakeholders. This would be section D on the graph above.

In smaller companies or startups, you might find yourself handling multiple or all of the responsibilities mentioned. Additionally, due to the evolving nature of the Data Science field, companies may assign different titles for various responsibilities, reflecting the flexibility in roles and the absence of standardized policies.

Q: What are some typical career paths for data professionals? Are there specializations within Data Science, and how do you navigate those choices?

The data landscape offers diverse career trajectories, allowing professionals to specialize based on their interests and strengths. Machine Learning model training often serves as a starting point, where individuals hone foundational skills before venturing into specialized domains. Specializations can range from Natural Language Processing (NLP) for text analysis to recommender systems, to anomaly detection or time-series forecasting in finance.

Similarly, Data Engineers may work in various industries, focusing on backend infrastructure and data architecture. Navigating these choices involves a blend of exploration and experience, with individuals gravitating towards areas that resonate with their passion and proficiency.

Q: For someone new to the field, which role might be easier to break into initially, and what foundational skills would you recommend?

Breaking into the data field often begins with roles like Data Analyst or Entry-level Data Scientist, where foundational programming skills and statistical knowledge serve as crucial entry points. While Data Analyst roles emphasize statistical hypothesis testing and data visualization, Data Engineers require proficiency in SQL and pipeline automation.

For aspiring Machine Learning Engineers, a deeper understanding of machine learning theory and programming skills becomes essential.

Regardless of the chosen path, a solid grasp of Python and SQL forms the bedrock, supplemented by continuous learning and upskilling in specialized areas as one progresses. Moreover, gaining practical experience through internships, personal projects, or participation in online communities can significantly bolster one's credentials and confidence in tackling real-world data challenges.

Additionally, cultivating soft skills such as effective communication, problem-solving, and teamwork enhances one's professional profile, facilitating smoother transitions and advancement within the dynamic landscape of the data industry.

Q: Can you describe a typical day or project for you as a Principal Data Scientist? How much time do you spend on math, coding, and other tasks?

As a Principal Data Scientist, my days are multifaceted, balancing technical endeavors with leadership responsibilities. While a portion of my time is devoted to coding and mathematical aspects, ranging from troubleshooting model logic to running experiments, a significant portion is also allocated to leadership and communication tasks.

Building relationships with cross-functional teams, providing architectural guidance, and facilitating project collaborations are integral aspects of my role. This blend of technical prowess and leadership acumen underscores the dynamic nature of data science roles at higher levels. This dynamicity extends beyond mere technical tasks; it encompasses strategic decision-making, resource allocation, and fostering innovation within the team.

Moreover, as a principal figure, there's a heightened sense of responsibility in ensuring that projects align with organizational goals and industry standards, further emphasizing the pivotal role of leadership in driving impactful data initiatives.

Q: What are the most rewarding aspects of being a Data Scientist?

The most fulfilling aspect of being a Data Scientist lies in the tangible impact one can make through data-driven insights and solutions. Witnessing projects come to fruition, whether it's a recommendation system serving millions or a model detecting anomalies, evokes a sense of fulfillment.

Knowing that the work contributes to enhancing user experiences or optimizing processes underscores the significance of data science in today's digital landscape. It's the fusion of technical expertise with real-world impact that makes the journey as a Data Scientist truly rewarding.

This sense of fulfillment extends beyond mere statistical analyses or coding tasks; it embodies the profound influence data science has on shaping industries, driving innovation, and improving lives. The ability to translate raw data into actionable strategies, be it for business growth, healthcare advancements, or societal progress, imbues the role of a Data Scientist with purpose and meaning.


Navigating the dynamic landscape of data science requires a blend of technical prowess, adaptability, and a passion for problem-solving. With diverse career paths and evolving specializations, aspiring data professionals have ample opportunities to make their mark in this exciting field.

  • Data Engineers focus on infrastructure, while Data Scientists and Machine Learning Engineers extract insights and deploy models, and Data Analysts synthesize insights through visualizations and reports.
  • Consider diverse career paths, with options like NLP, anomaly detection, and time-series forecasting aligning with individual passions and skills.
  • If you have less experience, it’s easier to break into roles like Data Analyst or Entry-level Data Scientist, requiring foundational skills in programming and statistics, than ML roles.
  • The most rewarding aspect of Data Science lies in seeing tangible impacts.
  • Data Science roles can offer fulfillment by significantly impacting industries, innovation, and societal progress through data-driven decisions.

Susan Shu Chang is a Principal Data Scientist at Elastic (of Elasticsearch), with previous ML experience in fintech, telecommunications, and social platforms. Susan is an international speaker, with talks at 6 PyCons worldwide and keynotes at Data Day Texas, PyCon DE & PyData Berlin, and O’Reilly’s AI Superstream. She writes about machine learning career growth in her newsletter, and is the author of Machine Learning Interviews, published by O’Reilly. In her free time she leads a team of game developers under Quill Game Studios, with multiple games released on consoles and Steam.

Check out Susan’s upcoming course, Pathway to Senior for Early ML & Data Professionals, starting 5/25. Use the promotion code “jeanlee” to get 20% off!

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