Top Tips to Land a Career in Data - From Leaders at Sephora and Stripe

Breaking into the field of data can seem overwhelming, but industry experts have the key. Extern recently hosted a webinar featuring Jane Wong, Senior Manager of Business Intelligence and Growth at Sephora, and Amla Srivastava, Senior Data Scientist at Stripe. Whether you're interested in machine learning, business intelligence, or predictive analytics, here are some of their top tips that will guide you toward building a successful career in data.

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1. Explore Different Data Roles

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The field of big data offers a variety of career paths, including:

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• Data Analysts – Specialize in SQL, data visualization, and A/B testing to extract insights
• Data Scientists – Build out models and tools for predictive analysis
• Data Engineers – Focus on ETL (extract, transform, load) processes and database management to structure large datasets and build efficient pipelines

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Each company structures its data teams differently, so do your research - look at job descriptions and explore multiple career paths.

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2. Build a Strong Technical Foundation

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‍Regardless of your chosen role, technical skills are essential. Start with:

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• SQL for querying and managing databases.
• Data visualization tools like Tableau and Power BI for presenting insights.
• Machine learning fundamentals for those interested in predictive modeling.

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Tackling these skills may seem daunting, especially if you majored in an unrelated field. Online learning and externships are a great, flexible way to gain knowledge and experience in these areas.

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3. Be Curious & Ask the Right Questions

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‍Great data professionals don’t just answer questions—they ask better ones. Having an intellectual curiosity about data will set you apart. Always question why certain trends exist and how they impact business decisions.

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Curiosity leads to better insights, making you more valuable in any data role and beyond.

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4. Understand Business & Industry Context

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‍Technical skills alone aren’t enough to stand out. Understanding business intelligence is key.

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• Retail companies use predictive analytics to optimize pricing strategies.
• Marketing teams leverage A/B testing and AI & automation to improve ad performance.
• Finance teams rely on data science and statistics & probability to assess risk.

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Employers look for candidates who can connect data analytics with real-world business challenges.

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5. Be Flexible & Open to Career Pivots

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‍Many professionals transition into data roles from different backgrounds. Your very first job does not define what you will be doing five years later. If your goal is data science but you don’t land a data science job immediately, try to gain experience in a related field like marketing, finance, or operations. You can transition over time, or your career path may take a pleasantly unexpected turn.

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Your first job doesn’t define your entire career, so stay flexible.

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6. Focus on Continuous Learning

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‍The field of data is constantly evolving, and it’s no surprise that AI and automation are reshaping how businesses approach data analysis. Keeping up with new trends in machine learning, predictive analytics, and business intelligence will help you stay competitive.

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Following industry leaders, taking online courses, and networking with professionals can help keep your skills sharp.

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Want to watch the full webinar?

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No problem. Hear more of Amla Srivastava and Jane Wong's thoughts from Land Your Dream Job in Data Ft. Amla and Jane here.