Christian Wanser

Data Wizard & Mentor | Helping data nerds launch & advance their data careers | Sr. Data Analyst | MS Analytics

(All of my content is my personal views and opinions) 

Thanks for checking out my page! I am a data professional with years of experience in data analysis, data visualization, machine learning, dashboarding, and other data requirements.

I am an engineer turned data expert with extensive experience providing insights through data. I discovered my passion for data while completing my B.S. in Chemical Engineering and I went on to receive my M.S. in Data Science & Analytics!

I am a strong critical thinker, with robust technical skills that are healthily balanced with intuition and an innate ability to communicate data. I live to see the lightbulb moment when I’m discussing topics with people!

How can we help each other?

Throughout my schooling and professional career, I have always enjoyed helping my peers and coworkers. With LinkedIn and Medium, I'm casting a wider net! Reach out, connect, follow, or just pass through. Whatever floats your boat and best of luck on your journey!

Sr. Data Analyst

Alliance Health

Senior Data Analyst - Population Health and Care Management

- Developed and maintained weekly productivity reports for the Care Management Team, ensuring timely and accurate insights.
- Crafted complex SQL queries to retrieve and consolidate data from the warehouse, providing a foundational reference for subsequent queries and analyses.
- Developed SQL queries to detect and mitigate potential compliance risks.
-Developed Python scripts to automate several manual reports, significantly increasing efficiency and reducing errors.
- Utilized version control systems to manage and track changes to SQL scripts and Python code, ensuring consistency and facilitating collaboration.
- Collaborated with various departments to understand data requirements and ensure the reports met organizational needs. Provided internal support and guidance in utilizing reports for tailored uses.
- Ensured productivity reports were accurate and validated by external vendors, enhancing trust and reliability in the data.
- Established productivity reports as a reliable source of truth used across the organization, supporting data-driven decision-making.
- Worked closely with stakeholders to gather relevant data and understand their priorities, delivering insights that informed strategic decisions.
- Identified opportunities for process improvements and implemented solutions to enhance data quality and reporting processes.

Data Analyst - Population Health and Care Management

LinkedIn

LinkedIn Learning Instructor

Dentalle Inc.

•	Tracked and maintained accurate records of all incoming orders, ensuring data integrity while streamlining order management workflows for efficiency
•	Analyzed patterns and trends in case submissions, identifying which types of cases were received the most and least frequently to inform customer service. 
•	Conducted office-level analysis to determine which dental offices submitted most cases, helping streamline communications and strengthen business relationships.
•	 Investigated patterns in offices that reduced or stopped case submissions, identifying root causes and collaborating with the sales and customer service teams to implement improvements
•	Generated reports and dashboards to visualize trends in case submissions and office performance, driving decisions using data to enhance operations efficiency and workflow improvements.

Data Specialist

HEALTHY FAMILY DENTISTRY PA

•	Reconciled claims with financial reimbursement, identifying and addressing discrepancies between expected clinical performance payments and actual reimbursement received
•	Analyzed claim denial trends, identifying root causes and implementing solutions to reduce rejections and improve overall revenue cycle management
•	Streamlined processes by collaborating with leadership to implement best practices and standardized workflows, enhancing efficiency across the organization
•	Led workflow improvements to reduce patient wait times, optimize scheduling, and ensure appropriate staffing during peak hours, improving overall patient care
•	Initiated process improvements that enhanced practice operations, including optimizing claim processing and staff utilization to support practice growth and efficiency
•	Monitored patient satisfaction, using data analysis to identify areas of service improvement and enhance the patient experience
•	Tracked outcomes to assess effectiveness and worked with clinicians to adjust practices based on data-driven insights
•	Developed preventive care programs by analyzing patterns in oral health issues and designing educational initiatives to promote better patient outcomes
•	Evaluated clinical staff performance by analyzing productivity, treatment efficiency, and patient outcomes to recommend targeted improvements and staff training.

Analytical Dental Hygienist

I’ve always seen data as more than numbers in rows and columns: it’s a story waiting to be told. But stories can get lost when the data is messy, scattered, or hard to trust.

That’s why I build modular, trustworthy data workflows that make stories clearer, more reliable, and ready to power both analytics and automation. Using SQL, Python, DuckDB, and Git, I design data systems that are reproducible, collaborative, and scalable, so data teams don’t just crunch numbers, they build knowledge that lasts.

My work blends engineering discipline with analytical curiosity. I focus on quality, clarity, and governance, especially in healthcare, where data decisions have real impacts on people’s lives. To me, data systems shouldn’t just aim for performance, they should also be responsible, supporting ethical AI, better patient outcomes, and more human-centric technology.

At the heart of it all, I believe clean, well-structured data is the foundation of every story worth telling, whether that’s a business insight, a machine learning model, or a decision that shapes someone’s future.

Thais Cooke

My content has always been about what I observe and learn, and this morning I finally tried Claude Code (Better late than never, right?)

Here is how my brain sounded in real time:
“Ok, I will probably break something… but that’s how you learn”. “Let me just peek at a tutorial real quick…” “Wait, this is actually really cool”. “I made a game in a few minutes?” “And it just… pushed to GitHub? I just called and it happened?” “I can’t go back to how I was doing things before”

The experience was so exciting. I can already see the value for prototyping, experimenting, and building things you wouldn’t have had the time to build before

But here is the thought that stuck with me most: AI doesn’t replace creativity, it amplifies. The ideas still have to come from somewhere. That somewhere is still you.

Oh, and my first project? Not exactly original, but very on-brand for an 80s kid. I had to build an arcade-style game. No regrets.

Senior Healthcare Data Analyst | LinkedIn Learning Instructor | Speaker

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Last week, I attended Snowflake's event Data For Breakfast in Raleigh. Among different themes, one caught my attention: the rate of AI adoption. People won't adopt AI in their workflows if the dominant narrative is that AI is coming for their jobs.

I have written about this before: this is not a technology problem, it's a trust problem. And to be fair, the fear isn't irrational. AI has already changed hiring in real ways across different industries. Some of the resistance is also legitimate professional judgment: concerns about accuracy, accountability, and what happens to hard-earned skills over time. Those concerns deserve to be taken seriously.

But here is where I think the conversation gets stuck: AI initiatives stall because the messaging from the top is still framed around efficiency and cost reduction. That narrative makes even willing adopters hesitant. It lands as "we're building the thing that replaces you."

People want to use these tools when it's shown to them that AI can make them more creative and help them build things they never thought possible or would have taken too long to do alone. The narrative shift that unlocks adoption is "AI will make you more capable."

The next frontier in data is cultural. The companies that figure this out first will be the ones who stop telling their people they're replaceable, and start showing them what they can become.

(I know this firsthand. Using AI in my own day-to-day work has unlocked possibilities I didn't think were possible before. But that's a topic for another post)

I've been thinking about how technology evolves. It always comes back to the problems we're trying to solve.

Need to get somewhere faster? We invented cars. The first ones were clunky and unreliable, but we kept refining them. We also learned the hard way that we needed rules, such as speed limits, driver's licenses, and safety standards, because powerful tools require responsible use.

Need to see inside the human body? We developed X-rays, then CT scans, MRIs, and ultrasounds. But the real breakthrough wasn't just the technology: it was learning when to use each one. Doctors don't order every test for every patient. They match the tool to the specific problem.

Which brings me to AI.

The models are getting really good. And even as the initial hype starts to fade, companies are still treating AI as a silver bullet. Right now, there's enormous pressure to slap "AI-powered" on everything. Investors want it. Shareholders demand it. Companies are racing to show they're using AI, and not because they've identified a problem it can solve, but because not using AI feels like falling behind. The hype has become about capturing market value, not creating human value.

The question isn't "what can AI do?" It's "what specific problem are we trying to solve, and is AI actually the right tool for this problem?"

Sometimes it is. AI can spot patterns in medical imaging that humans miss, but you still need a human to make the final decision. It can process climate data at scales we never could before, but scientists need to interpret what those patterns mean and decide how to act. It can make knowledge more accessible to people who were previously left out, but only if we design it with their actual needs and contexts in mind.

And sometimes the better answer is a clearer process, a simpler spreadsheet, or just talking to the people affected by the problem. Technology for its own sake isn't progress.

If we want to use AI for humanity's improvement, we need to start with humanity. What do people actually need? Who's being heard in these decisions? And are we building the accountability and safeguards that we know from history, powerful technologies require?

The first cars were funny-looking and didn't work very well. But we also didn't hand out keys to everyone and hope for the best. We shouldn't do that with AI either.

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Christian Wanser

Thais Cooke

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The Battle

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Thais Cooke

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Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Thais Cooke's LinkedIn Performance

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