Most healthcare data leaders aren’t struggling with analytics.They’re struggling with inherited chaos — 10+ years of conflicting definitions, EMR exports nobody trusts, and leadership demanding answers from numbers that don’t align.

I partner with the people living that world every day: VPs of Analytics, Directors of IT
and cross-functional data leaders who are drowning in Excel reconciliation, political misalignment, and dashboards that spark more debate than decisions.

If your finance, operations, and EMR teams all have different definitions of “Net Revenue,” I fix that.

I build the data foundations that make AI possible — and trustworthy.

DM me "AI in Healthcare" if you're ready to get your data foundation in order.

Christian Steinert

I've pitched data analytics to pre-seed startups and multi-billion dollar enterprises.
 
The objection is always the same.
 
"We'll invest in data once things settle down."
 
Things never settle down.
 
And bad data keeps compounding in the background while they wait.
 
Here's what they're not seeing:
 
Gartner estimates poor data quality costs the average enterprise $12.9 million annually.
 
McKinsey puts it differently — bad data drives a 20% drop in productivity and a 30% spike in costs.
 
That's not a technology problem.
 
That's a profit problem hiding in plain sight.
 
𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝘆 𝘄𝗲 𝗯𝘂𝗶𝗹𝘁 𝘁𝘄𝗼 𝗲𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗺𝗼𝗱𝗲𝗹𝘀:
 
For organizations ready to fix it at the root — a full data transformation initiative. Metrics clarity across every department. One source of truth.
 
For organizations not quite there yet — a narrowly scoped analytics product built around one burning business question. Fast value. Low friction. Proof of concept.
 
𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆:
 
You don't have to boil the ocean to start fixing the problem.
 
But you do have to start.
 
Every month you wait is another month that $12.9M stat applies to you.
 
♻️ Repost this if you know a leader who still sees data as optional.
 
Follow me for real talk on making data drive outcomes.

I help healthcare data leaders with inherited chaos fix broken definitions and build AI-ready foundations they can finally trust. | Host @ The Healthcare Growth Cycle Podcast

I violated data best practices to deliver a $40K ROI.

(The client renewed. Here's why.)
 
For 4 years, I've preached data best practices:
 
Build proper data models. Minimize tech debt. Do it right the first time.
 
Then reality hits.
 
A mid-sized healthcare company hires us.
 
They need a manual report automated. Fast.

Your offer as a consultant is speed-centric.
 
Their "source of truth" is 400 stored procedures written by a DBA who left 2 years ago.
 
Zero documentation. Spaghetti SQL everywhere.
 
30+ Power BI reports querying directly off the transactional database.
 
𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝘄𝗮𝗻𝘁𝗲𝗱 𝘁𝗼 𝗱𝗼:
 
Build a clean data warehouse from scratch.
 
Proper dimensional modeling. Governed metrics. Best practices.
 
𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗶𝗱:
 
Replicated their messy legacy logic in the cloud.
 
Matched their numbers exactly—even the parts I knew were questionable.
 
Automated the manual report in 6 weeks.
 
Delivered the $40K ROI we guaranteed.
 
𝗪𝗵𝘆?
 
Because many executives don't care about best practices.
 
They care about results. Now.
 
You don't get 3-6 months to "do it right." You get 6 weeks to prove you're worth keeping.
 
𝗧𝗵𝗲 𝘁𝗿𝘂𝘀𝘁-𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗽𝗮𝗿𝗮𝗱𝗼𝘅:
 
If you show up and tell them their legacy logic is wrong, they won't trust you.
 
If you replicate it perfectly first, they do.
 
Once trust is built? Then you can challenge the legacy logic.
 
Then you can propose the proper data model.
 
Then you can start fixing the mess.
 
But not before.
 
𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗯𝗮𝗹𝗮𝗻𝗰𝗲 𝘀𝗽𝗲𝗲𝗱 𝗮𝗻𝗱 𝗾𝘂𝗮𝗹𝗶𝘁𝘆:
 
𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗾𝘂𝗶𝗰𝗸 𝘄𝗶𝗻𝘀 𝘁𝗵𝗮𝘁 𝗲𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝘁𝗿𝘂𝘀𝘁
 
Automate one critical report. Match legacy numbers. Show ROI fast.
 
𝗢𝘃𝗲𝗿𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝘁𝗵𝗲 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳𝘀
 
"This works, but it creates tech debt. Here's the plan to fix it long-term."
 
𝗖𝗮𝗿𝘃𝗲 𝗼𝘂𝘁 𝘁𝗶𝗺𝗲 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗿𝗲𝗯𝘂𝗶𝗹𝗱
 
Once trust is established, allocate hours to build the proper foundation.
 
𝗞𝗲𝗲𝗽 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝘃𝗮𝗹𝘂𝗲 𝘄𝗵𝗶𝗹𝗲 𝘆𝗼𝘂 𝗶𝗺𝗽𝗿𝗼𝘃𝗲
 
Don't stop showing ROI while you refactor. Balance both.
 
𝗧𝗟;𝗗𝗥:
 
Best practices are the North Star.
 
But speed to value is survival.
 
Deliver quick wins. Build trust. Then improve the foundation.
 
Perfection kills consulting businesses. Progress builds them.

Agree or Disagree?
 
♻️ Share this if you've ever had to choose between doing it "right" and doing it "fast."
 
Follow me for real talk on what data consulting actually looks like in the wild.

A year ago this month I made no money.
 
Zero. February 2025 was the lowest point of my entrepreneurial journey.
 
I'd left a stable Senior Analytics Engineer role at JLL to bet on myself in August 2023.
 
Started by subcontracting. 

Built enough credibility to land contracts with a top 5 fast food giant and a FinTech company as a lead Looker consultant.
 
But I wanted to build something of my own.
 
So I pivoted to direct.
 
And it was brutal at first.
 
I landed my first direct client from a CEO I train with at my gym back in late 2023 so I had a baseline.
 
Then I spent the first months of 2025 sending 1,365 cold LinkedIn outreaches manually.
 
5 follow up sequences. Each one. Daily.
 
Most never responded.
 
Some said no.

Some became annoyed.
 
One became a pillar client that changed everything.
 
𝗪𝗵𝗮𝘁 𝗜 𝗿𝗲𝗯𝘂𝗶𝗹𝘁 𝗶𝗻 𝟭𝟮 𝗺𝗼𝗻𝘁𝗵𝘀:
 
My go-to-market strategy. My sales process. My lead gen approach.
 
And my niche — planting my flag completely in healthcare data.
 
𝗙𝗲𝗯𝗿𝘂𝗮𝗿𝘆 𝟮𝟬𝟮𝟲:
 
Multiple active client engagements.
 
A podcast. A newsletter. A Columbus event. A growing brand on here.
 
And a business that finally reflects the vision I left corporate for.
 
The grind was worth it.
 
If you're in your February 2025 season right now — keep going.
 
The breakthrough is closer than it feels.

Shout out to Brian Thomas for being a guiding light, helping and developing me into a truly bullet proof entrepreneur.
 
♻️ Repost this if you've ever bet on yourself when the numbers said don't.
 
Follow me for the unfiltered journey of building a data consulting company.

Microsoft Support responded 24 hours after I needed them.
 
Finance needed numbers at 8 AM. It was 9 PM the night before.
 
Here's what happened:
 
We reorganized our Microsoft Fabric pipelines into folders for cleanliness.
 
Simple housekeeping. Routine maintenance.
 
What we didn't know — renaming those pipelines offset the metadata and triggered Access Error 5 on the Fabric Lakehouse SQL Endpoint.
 
Suddenly nothing could query the lakehouse.
 
Financial data. Gone dark.
 
𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻:
 
Microsoft Support SLAs don't align with business SLAs.
 
I opened a ticket. They responded 24 hours later with a fix.
 
That wasn't an option.
 
So I rebuilt the entire lakehouse from scratch overnight.
 
Repointed every pipeline. Every copy job. Every orchestration.
 
Finished at 2 AM.
 
𝗧𝗵𝗲 𝗹𝗲𝘀𝘀𝗼𝗻𝘀:
 
Document every pipeline naming convention before reorganizing.
 
Never assume a "simple" housekeeping task is low risk in Fabric.
 
Build redundancy. Support tickets won't save your 8 AM deadline.
 
𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆:
 
In data consulting, you don't always get to wait for help.
 
Sometimes you just have to rebuild.
 
♻️ Repost this if Microsoft Support has ever been too late to the party.
 
Follow me for real talk on the unglamorous side of data consulting.

I've kept quiet about this. Until now.
 
I recently started 1:1 datapreneurship coaching.
 
One of my current mentees is a beast. Former senior data engineer at a Fortune 200 tech company. Technically elite. Ready to build something of his own.
 
In our second session we got into go-to-market strategy.
 
Specifically — inbound vs. outbound lead generation.
 
Here's the hard truth I laid on him:
 
LinkedIn content builds top of funnel credibility.
 
But if you're not coming from Director of Analytics at Big Tech or Head of Data at a Fortune 100 — it won't fill your pipeline on its own.
 
Without that pedigree, content is the warm up act.
 
Raw outbound is the main event.

Leverage warm and cold.
 
𝗧𝗵𝗲𝗻 𝗵𝗲 𝗮𝘀𝗸𝗲𝗱 𝗺𝗲 𝗵𝗼𝘄 𝘁𝗼 𝗼𝘂𝘁𝗿𝗲𝗮𝗰𝗵 𝗯𝗲𝘁𝘁𝗲𝗿.
 
My answer:
 
Stop thinking about what you can get from a prospect.
 
Ask yourself what you can give them.
 
Can you feature them on a podcast? Invite them to an event? Give them a platform?
 
Case studies build credibility.
 
Genuine generosity builds relationships.
 
And relationships close deals.
 
𝗪𝗵𝘆 𝗵𝗲 𝗰𝗵𝗼𝘀𝗲 𝗺𝗲 𝗮𝘀 𝗵𝗶𝘀 𝗰𝗼𝗮𝗰𝗵:
 
Not because I came from a big title.
 
Because I built this without one.
 
Same as him.
 
That's exactly the datapreneur I coach.
 
Technically strong. Hungry to build. No corporate pedigree required.
 
If that's you — my DMs are open.
 
♻️ Repost this if you're building a consulting brand from scratch.
 
Follow me for the unfiltered playbook on datapreneurship.

I've started enough client engagements to know what day 1 should feel like.
 
It never starts that way.
 
We'd just kicked off a Data Foundation Sprint.
 
The client was excited. We were ready to move.
 
4 weeks to audit their entire data infrastructure, document every system, and begin cataloging their ecosystem.
 
But we couldn't touch anything.
 
No Power BI workspace access. No Microsoft Fabric permissions. No VPN. No database credentials.
 
Day 1 gone. Just like that.
 
𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻:
 
When you're running a 3-4 week sprint, every day matters.
 
Losing day 1 to access issues doesn't sound catastrophic.
 
But it kills momentum before you even build it.
 
And momentum in a short engagement is everything.
 
𝗧𝗵𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻:
 
We now send every new client a pre-kickoff onboarding checklist.
 
Before we ever sit down together — user accounts are created, permissions are granted, VPN access is configured, workspace invites are sent.
 
Day 1 is now reserved for actual work.
 
Not waiting rooms.
 
𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆:
 
Don't let logistics steal your momentum.
 
Build the checklist. Send it early. Show up ready to sprint.
 
♻️ Repost this if access delays have ever slowed down your team.
 
Follow me for real talk on running lean, fast data engagements.

3 signs a client isn't ready for transformation. 

(And what to do instead of walking away.)
 
I recently started an engagement with a fast-growing startup.
 
5 years of aggressive growth.
 
Impressive revenue numbers.
 
But their data foundation? Built with toothpicks and bubble gum.
 
A CRM, an accounting system, and spreadsheets stitched together by workarounds.
 
Dashboards bending to broken processes instead of fixing them.
 
Here's what the warning signs looked like:
 
𝗦𝗶𝗴𝗻 𝟭: They rejected the foundational engagement immediately.
 
Not after thinking about it. On the spot.
 
When a prospect flinches at the solution that fixes the problem — that tells you everything about their readiness.
 
𝗦𝗶𝗴𝗻 𝟮: Internal stakeholders wanted to handle it themselves.
 
Consultants thrive where there's buy-in.
 
When technical leadership sees you as an outsider — you're fighting uphill before you start.
 
𝗦𝗶𝗴𝗻 𝟯: Their architecture bent to their broken processes.
 
They didn't build systems to drive the business.
 
They built systems to survive it.
 
That's not a data problem. That's an organizational maturity problem.
 
𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆:
 
These signs don't mean walk away.
 
They mean right-size the engagement.
 
We rolled off after the maintenance contract ended. Added value where we could and let them run with it.
 
But when they're ready to build real infrastructure in a year or two?
 
They'll know exactly who to call.
 
♻️ Repost this if you've ever right-sized an engagement for a client who wasn't ready.
 
Follow me for real talk on the consulting side of data leadership.

I'm honored to be attending Podcasting Live 2026 this week. 🔥

The Healthcare Growth Cycle Podcast is our cornerstone platform for scaling healthcare execs.

Excited to maximize this show for my guests and listeners! 

Shout out to John Marsch for his vision building this conference right here in Columbus, Ohio. 

A great week ahead! 🎙️

Finch AI

- Collaborated with stakeholders, project managers, and other data scientists, to design data collection and LLM development efforts to improve NLP models in the backend of publicly facing software solutions.

- Led AI teams of data scientists end-to-end from data curation to model deployment to update to our model offerings, accomplishing a 25-point increase in model performance and heightened customer satisfaction.

- Incorporated LLMs and generative AI models, such as GPT-3.5, GPT4, Mistral, LLAMA, etc., into product offerings utilizing prompt engineering, RAG, and vector stores like Chroma DB, leading to increased work throughput.

- Utilized AWS EC2 instances and Linux to automate training and evaluating pipelines for NLP and NER models.

- Sourced, validated, and managed high-quality data for model training and development.

- Mentored junior data scientists providing them technical guidance resulting in improved cross-team learning and collaboration.

Data Scientist

University of Colorado Boulder

- Co-created an error analysis Python library to aid researchers’ hate speech detection natural language processing (NLP) models so that more ethical AI systems can be built.

- Researched the most current machine learning, neural network, and deep learning models to understand where limitations are in current research and design the library to facilitate in understanding where such models fail linguistically.

- Documented, co-wrote, and submitted an accepted paper to EACL 2023 about the Python library and appropriate use cases.  The library can help models improve by at least 5 percent.

NLP Researcher

Brandeis University

- Built, trained, and fine-tuned machine learning models in R and Python via traditional statistics-based data science techniques, Bayesian logistic regression, feature engineering, and unsupervised clustering algorithms, saving $50,000 in project costs and shaving 11 months off research and development time.

- Defined project goals to stay in close alignment with business needs and goals to timely deliver models, insights, and data visualizations to customers. 

- Automated repetitive workflows and processes to swiftly iterate models and their reliability, as well as simplifying and streamlining the supporting code bases.

- Clearly communicated technical concepts to key business stakeholders with data-driven insights and A/B testing to influence the direction of model development.

- Ensured data governance by managing the Linux machines which contained model data and updating documentation.

Luminoso Technologies, Inc.

- Leveraged Japanese linguistic knowledge, drastically improving NLP sentiment analysis by over 50% for Japanese text data, leading to the product selling to a Japanese audience. 

- Articulated and arranged relevant Japanese to English translations, thereby refining and analytic processes by 20%.

NLP Intern

My name is Jay Seabrum and I am an experienced and passionate data scientist driving innovation in the field of machine learning and AI with 4 years of working in small teams, solving complex and real-world end-to-end problems.  Prior individual contributions include: over $50,000 saved in costs via innovative and efficient processes, staying up to date with advancements in AI and ML, leading teams in developing machine learning models, and a decrease in model iteration and experimentation time. Aiming to further apply expertise in a senior or mid-level data scientist role, or more leadership oriented roles.  Open to relocation.

You can contact me in one of three ways:  (1) schedule a meeting on calendly.com/seabrum , (2) send a message with your connect request, or (3) send an email to seabrum.x@gmail.com and I'll be sure to get back with you.  Please note that (1) I do not accept connect requests without a message attached to them and (2) I am not interested in pure software engineering roles. Thanks!

#datascience #datascientist #nlp #machinelearning #natural #language #processing #ai

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Christian Steinert

Xajavion Seabrum

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