ADHD at Work: Balancing Curiosity and Focus as a Data Scientist
- CJ Pringle

- Sep 23
- 3 min read
Why Data Scientists Are Essential in Modern Workplaces
Data scientists are the investigators and interpreters of the modern workplace. They collect, clean, analyze, and visualize data to extract insights that drive business decisions, forecast trends, and build machine learning models.
Working at the intersection of statistics, programming, and strategy, they often collaborate across departments to solve complex problems. Their role is essential for organizations to make evidence-based decisions, improve efficiency, and innovate. Whether in healthcare, tech, finance, or retail, data scientists turn messy datasets into clear, actionable stories.
September 2025, CJ Pringle, ADHD Coach @ Agave Health

How ADHD Symptoms Show Up in Data Science
Data science is deeply rewarding for ADHDers who love solving puzzles and following curiosity—but it can also be a minefield for executive function challenges.
1. Getting Stuck in Hyperfocus (and Losing Track of Time)
Cleaning a dataset or tweaking a model can pull ADHDers into deep flow states where hours slip away.
This may lead to missed meetings, skipped meals, or neglected priorities.
2. Avoiding Tedious or Repetitive Tasks
Data wrangling, documentation, and unit tests can feel boring and mentally exhausting.
ADHDers may procrastinate or leave small-but-crucial tasks unfinished.
3. Struggling with Open-Ended Problem Solving
Many data problems don’t have a clear end point, which can cause analysis paralysis.
ADHD makes it harder to know when to move forward or stop tinkering with models.
4. Working Memory Gaps During Complex Projects
ADHD impacts working memory, making it difficult to recall key variables, steps in a pipeline, or function structures.
Without external systems, this leads to wasted time retracing steps.
5. Inconsistent Documentation and Communication
From code comments to stakeholder reports, clear communication is essential.
ADHD can result in skipped notes, incomplete reports, or forgotten follow-ups, which impacts collaboration.
How Data Scientists with ADHD Can Stay Focused and Organized
The field of data science values curiosity, creativity, and problem-solving—all ADHD strengths. Here’s how to manage the executive function side:
1. Break Down Projects into “Data Chunks”
Divide workflows into stages: acquisition, cleaning, exploration, modeling, visualization, and reporting.
Use checklists or Kanban boards to visualize progress and stay on track.
2. Use Code Comment Prompts
Add reminders as you code (#TODO: optimize this later, #Check logic here).
These breadcrumbs help ADHDers reorient quickly after breaks.
3. Set Time Blocks with Exit Alarms
Use countdown timers or calendar reminders to avoid time spirals when hyperfocusing.
Even a 2-minute alarm can help reset.
4. Create Reusable Templates
Build templates for notebooks, reports, slide decks, and model evaluations.
Reduces friction and boosts consistency without reinventing the wheel.
5. Build a “Data Dictionary” or Project Log
Keep a quick-reference doc with key variables, assumptions, sources, and explored dead ends.
Prevents wasted time and supports memory.
6. Use Visuals to Stay Engaged
Create charts early in exploration for clarity and reinforcement.
Visuals keep ADHD brains engaged and help spot insights sooner.
How Agave Health Supports Data Scientists with ADHD
Data science can be both exciting and overwhelming—especially when ADHD turns small tasks into big hurdles or makes it tough to follow through. That’s where Agave Health comes in.
Our ADHD-informed coaching and therapy programs are designed to help data scientists:
Bring structure to complex, open-ended projects
Build stronger time awareness and reduce overthinking
Create consistent systems for documentation and reporting
Work through perfectionism and decision paralysis
Leverage ADHD strengths like creativity, pattern recognition, and curiosity
At Agave Health, we understand the unique challenges ADHD professionals face in fast-paced fields like data science. With the right support, you can turn scattered energy into sustainable progress—so your curiosity fuels innovation instead of burnout.



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