Interview: A Data Scientist Explains What They Actually Do (and What They Wish They Learned at 12)

Meet the Data Scientist: “My Job Is Turning Messy Information Into Decisions”

Parents hear “data science” and picture someone staring at complicated charts all day. Kids hear it and think it’s either impossible… or basically just “being good at math.” The truth is more interesting (and more reachable).

For this interview, I sat down with Jordan Lee, a data scientist who works with an education technology team. Jordan helps build features that recommend lessons, spot when students are getting stuck, and measure whether new learning activities actually work.

Maya (Intellect Council): If you had to answer the big question—what does a data scientist do—how would you explain it to a 12-year-old?

Jordan: “A data scientist is someone who uses data to answer questions and make better decisions. It’s like being a detective, but your clues are numbers, text, and patterns in real life.”

Maya: What kinds of questions do you answer at work?

Jordan: “Questions like: Which lesson order helps kids learn faster? What type of practice makes a concept stick? Why do some students quit after a certain point? Data science is not just predictions—it’s understanding what’s happening and what we should do next.”

Jordan’s take is parent-friendly and honest: a data scientist isn’t a robot doing math. They’re a problem-solver who communicates, experiments, and builds tools that help people.

A Day-in-the-Life: What Data Scientists Actually Do (Not Just “Build Models”)

A common myth is that data scientists spend all day building fancy AI models. In reality, a lot of the work is more “real world” and surprisingly relatable.

Here’s how Jordan broke down a typical week:

• Clarify the goal (the “why”)
  • “Before touching data, we define what success means. Otherwise you can prove anything.”
• Gather and clean data (the “make it usable” step)
  • “Most data is messy: missing values, weird labels, duplicates. Cleaning is where quality happens.”
• Explore patterns (the “what’s going on?” phase)
  • “We make charts, check trends, and ask: what looks surprising?”
• Test ideas with experiments (the “prove it” step)
  • “Sometimes we run A/B tests—like trying two versions of a lesson and measuring results.”
• Build a simple model when it helps
  • “A model is just a tool. Often a simple approach beats a fancy one if it’s clearer and reliable.”
• Explain results to humans
  • “If I can’t explain it to a product manager or teacher, it doesn’t matter.”

What parents should know: communication is a major part of the job. Jordan said the strongest data scientists they know can “translate” between numbers and real decisions.

The “hidden” skills Jordan uses daily

These are the skills families don’t always associate with data science, but they matter a lot:

• Writing clear notes and summaries (yes, writing)
• Asking good questions (curiosity)
• Organizing work (planning, version control)
• Handling uncertainty (being okay with “we need more data”)

If you’re wondering about skills needed for a data science career, notice how many are learnable early—and don’t require advanced calculus in 7th grade.

What Jordan Wishes They Learned at 12 (So They’d Be Ahead Now)

This was my favorite part of the interview because it turns “data science” into something families can actually do.

Maya: If you could go back, what would you learn at 12?

Jordan: “I’d learn to work with data in small ways—like keeping track of something, making a graph, and explaining what it means. I’d also learn basic coding earlier, not to be fancy, but to be confident.”

Jordan shared four specific things they wish they’d practiced earlier:

• 1) Comfort with spreadsheets
  • “If you can sort, filter, and make a chart in a spreadsheet, you’re already doing data work.”
• 2) Asking measurable questions
  • Instead of: “What’s the best study method?”
  • Try: “If I study 20 minutes/day vs. 40 minutes/day for two weeks, does my quiz score change?”
• 3) Basic probability and logic
  • “You don’t need advanced math. You need to understand chance, averages, and ‘is this fair?’”
• 4) Learning from mistakes (debugging mindset)
  • “Data science is constant debugging—of code, assumptions, and messy data.”

This is why data science for middle school students can be a smart idea: it’s not about rushing into college-level topics. It’s about building habits—questions, evidence, and clear thinking.

A simple “middle school data science” challenge (you can do this this weekend)

Pick one question and track it for 7–14 days:

• Which snack keeps me full the longest?
• Does practicing math in the morning or afternoon work better?
• What music helps me focus (or distracts me)?
• How does sleep change my mood or energy?

Then:

• Create a tiny dataset (date, choice, result)
• Make one chart
• Write 3 sentences: what you expected, what happened, what you’d test next

That’s real data science.

How to Become a Data Scientist: A Kid-to-Teen Roadmap (With Real Skills)

If your child is interested, it helps to see a path that’s concrete. Jordan emphasized that there isn’t one “perfect” route. But there are common building blocks.

Below is a practical roadmap with age-appropriate steps. It’s designed to grow confidence, not pressure.

Age band	What to learn	Fun project idea	Why it matters for a data science career
10–12	Charts, averages, simple probability, spreadsheets	Track a hobby (sports stats, reading minutes) and graph trends	Builds data intuition and “tell the story” thinking
12–14	Intro coding (Python or Scratch-to-Python), data tables, basic statistics	Analyze a class survey (favorite games, sleep hours)	Connects questions → data → conclusion
14–16	Python basics, data cleaning, visualization, simple models	Predict something simple (like study time → quiz score)	Teaches the full workflow, not just math
16–18	Projects with real datasets, ethics, experiment design, communication	Build a dashboard or write a report from open data	Shows readiness for internships and portfolios

The core skills needed for a data science career (in plain English)

Jordan listed the “must-haves” like this:

• Data skills: collect, clean, organize, and visualize data
• Coding skills: write code that works and is readable
• Math skills: understand averages, distributions, probability, and basic algebra (calculus can come later)
• Communication skills: explain what you found, what it means, and what you recommend
• Ethics: notice bias, privacy issues, and “should we do this?” questions

Parent tip: If your child likes debate, writing, art, sports, or social science, they can still thrive in data science. The field needs people who can interpret human behavior—not just crunch numbers.

One more thing Jordan wants families to know

Maya: Any misconception you wish would disappear?

Jordan: “That you have to be a genius. You don’t. You need consistency and practice. Also, real data is messy—feeling confused at first is normal.”

That’s a powerful message for kids (and parents). Confidence grows when kids realize confusion is part of the process, not proof they don’t belong.

Next Steps: How to Get Started This Month (Without Overwhelm)

If your child is curious about data science, here’s a simple, action-oriented plan you can actually follow.

• Week 1: Pick a question
  • Choose something your child genuinely cares about (games, sports, sleep, reading, pets).
• Week 2: Collect tiny data
  • Keep it small: 10–30 rows is enough.
  • Use a notebook or a spreadsheet.
• Week 3: Make 2 charts and a mini-story
  • One line chart (change over time) and one bar chart (compare categories).
  • Write a short conclusion: “I noticed…, I think…, next I would…”
• Week 4: Add a coding step (optional but exciting)
  • Try recreating one chart in beginner-friendly Python.
  • The goal is not perfection—the goal is “I can make the computer do something useful.”

If you want extra structure, look for learning experiences that:

• let kids interact, test, and iterate (not just watch videos)
• give quick wins (small projects with visible results)
• reward consistency (short sessions beat marathon weekends)

Data science is a career where curiosity pays off. Starting at 12 doesn’t mean racing ahead—it means building a foundation that makes everything later feel possible.