Kedeisha Bryan

Data In Motion, LLC

Founded and scaled a data analytics education platform, designing and launching bootcamps, upskilling programs, and coaching initiatives to empower career changers and professionals in breaking into data.

- Empowered over 15,000 students by building a thriving data analytics education platform, providing hands-on courses, personalized coaching, and job placement support.

- Facilitated job offers by designing and launching high-impact bootcamps and upskilling programs, with graduates securing roles at Microsoft, phData, and UT Austin.

- Achieved an 85% student success rate by managing a team of 5 mentors and 50 bootcamp students, ensuring structured learning, mentorship, and career placement support.

- Refined target customer strategy by developing and analyzing 50+ surveys, gathering critical insights on customer needs and market trends using descriptive statistics, customer segmentation and NLP.

Founder & CEO

LinkedIn

LinkedIn Learning Instructor

COLSA

Designed, and optimized ETL pipelines, clustering models, and performance measurement frameworks, leveraging Python, MongoDB, and TensorFlow to enhance data processing, anomaly detection, and model evaluation.

✓ Utilized python and TensorFlow to develop ETL pipelines to process unstructured data for object detection models.
✓ Improved anomaly detection by refining and updating dimensional clustering models, leveraging statistical techniques to
enhance cluster quality.
✓ Created performance measurement frameworks for digital signals data

Data Scientist

Booz Allen Hamilton

Senior Data Visualization Specialist Consultant 

• Used python in Databricks to clean over 1 million rows of public government data. 
• Utilized data cleaning methods with the pandas library to treat missing and incorrect data.
• Built data pipelines in Microsoft Azure

Senior Data Engineer 

Humu

Led data extraction, customer analytics, and process optimization initiatives, leveraging SQL, Python, dbt, Google BigQuery, and Six Sigma methodologies to enhance decision-making and business efficiency.

- Increased data processing efficiency 30% by developing and optimizing SQL queries for production-quality data extraction, improving analysis of large datasets. 

- Enhanced customer retention strategies by analyzing CRM data to identify key predictors of customer health, providing actionable insights for business teams.

- Improved product decision-making by generating data-driven insights into product usage trends, leveraging Python and Google BigQuery to inform strategic enhancements.

Data Science Intern

Correlation One

• Led additional lessons in data analysis techniques in Excel, Tableau, and SQL.
• Mentored over 40 fellows in their transition into the Analytics field

Data Analytics Teaching Assistant

Cox Communications

- Detected $8M in annual fraud through exploratory data analysis, developing an interactive Tableau dashboard to communicate findings to executive leadership.

- Reduced report generation time from 4 hours to 15 minutes by leading the automation of business reporting, significantly improving operational efficiency.

- Optimized business operations by managing cross-functional projects, applying data analysis, Lean Six Sigma, and research-driven insights to drive process improvements.

Skills: Python · SQL · Microsoft Excel · Tableau

Data Visualization Analyst Co-op

US Navy

- Successfully managed a team of 15 technicians. Increased productivity of 30% through improving our end-to end service procedures.

- Optimized efficiencies by implementing Lean Six Sigma methods such as 5S that drove efforts to transform the work center, reducing waste and downtime by 25% and increase production availability by 30%

Aviation Electronics Technician

Founder at Data in Motion LLC | Senior Data Visualization Consultant | Navy Veteran | Top 30 Women in Data | Building the best skill & career development platform for the data community

datacareerblueprint.com || Hi I'm Kedeisha, a data scientist and career strategist who went from delivering pizzas to leading a 55,000-strong data community. 

Over the years, I’ve helped thousands of professionals land job offers at top companies like Microsoft, phData, and the University of Texas at Austin—and many of them have climbed to senior roles in under three years.

As the author of Cracking the Data Engineer Interview, I’ve seen firsthand how the right strategies can transform your career. 

My mission? To share everything I’ve learned about breaking into and thriving in the data world, from mastering your skills to acing interviews and advancing your career.

I offer a tailored coaching program for aspiring data analysts that provides:

✅ Master SQL, Excel, Tableau, Python, and more in high-demand courses.

✅ Elevate your brand with LinkedIn and personal brand coaching.

✅ Create a standout portfolio with real-world projects.

✅ Interview preparation.

✅ Develop your confidence and leadership skills 

Ready to land your dream job in data?

Online bootcamp and interview preparation program to kickstart a person's data career in 5 months without going back to school

Job descriptions often list every skill imaginable and many times, the company doesn’t even know what they truly need.

Here’s the reality:

You don’t need to master Python, SQL, Tableau, ETL, AI, ML, and every other tool mentioned.

Most candidates get hired because they can:
- Pull data with SQL
- Build a clear, effective dashboard
- Communicate insights in a way that makes sense

That’s it.

Don’t let job postings convince you that you’re not ready.

Focus on demonstrating that you can solve real business problems. 

That’s what actually leads to an offer.

👇🏽 What’s the most unrealistic job requirement you’ve ever seen?

I used to deliver pizzas, now I deliver insights | I help career changers launch $100k analytics careers without going back to school

You keep applying to data analyst roles… and hearing crickets.

There’s a reason for that.

Today, I’m breaking down exactly what you need to do before you hit “apply” so you stop blending in with every other candidate and finally start getting interviews.

Most people are making the same mistakes over and over again.

And wondering why nothing changes.

Watch this and fix it.

Your future hiring manager will thank you.

Stop using LinkedIn Easy Apply if you actually want to get hired as a Data Analyst.

Here's the truth nobody's telling you:
Easy Apply is where resumes go to die.

You're competing with 500+ other applicants who clicked the same button in 3 seconds. Zero differentiation. Zero context. Zero chance.

Meanwhile, the analysts who ARE getting hired? They're not playing the volume game.

They're building proof.

Real projects. Real skills. Real experience that makes recruiters stop scrolling.

So if you're done disappearing into the Easy Apply black hole and ready to actually stand out…

Comment "READY" and I'll send you my exact framework on how to get hired as a Data Analyst.

Live Data Analyst Resume Review with REAL Tech Recruiters 🚨

If you’ve been applying to data roles and hearing crickets, this session is your reality check.

We’re breaking down real resumes LIVE so you can finally see:

- What makes a recruiter stop scrolling
- What gets you rejected in under 10 seconds
- How to position yourself like a top-tier data analyst

If you’re serious about landing interviews in 2026, you cannot afford to keep guessing.

Bring your resume. Bring your questions.

You’ll walk away knowing exactly what to change, what to highlight, and what to delete.

Emily Nelson de Velasco and 46 other attendees

Live Data Analyst Resume Review with REAL Tech Recruiters

The degrees is no longer the golden ticket.

They cost more than ever, pay off less than ever, and 63% of Americans now say they aren't worth the price. 

The biggest reason? 

Graduates still lack the specific, job-ready skills needed to succeed. The Real Game: It’s not about the diploma. It’s about the direct, applicable skills and solving problems.

Are you seeing this shift too? Drop your thoughts in the comments.

Greg Coquillo

Product Leader @AWS | Startup Investor | 2X Linkedin Top Voice for AI, Data Science, Tech, and Innovation | Quantum Computing & Web 3.0 | I build software that scales AI/ML Network infrastructure

Product leader with a passion for solving business use cases such as Growth, Go-to-Market, and Price Optimization. I owned P&L for commodity-based products, and leveraged AI to increase the average customer contract values across business industries. As a Product Leader at Amazon, I own a roadmap that drives the adoption of AI products through the Media Automation charter, which supports Private Brands’ YOY growth. I work with Data Scientists, Software Engineers and Business Intelligence Engineers to leverage Machine Learning and Big Data Analytics to design predictive models that enhance business decision-making and performance to accelerate growth, reduce Time to Market of products, monitor cross-geo compliance and import requirements, and ensure customer safety.

I am always curious and eager to learn how new technology affects our daily lives, and I love to support communities on their quest to adopt Artificial Intelligence across industries. Feel free to contact me if you have Data Science projects that you’d like to be showcased on this platform for visibility.

🌐I am a Global Business Leader with 12 years of experience and skills including Product Management, P&L management, Global Networking, EBITDA Optimization, Pricing Management, Margin Management, Product Management, Program Management, Operations and Supply Chain Management, Lean Manufacturing, Six Sigma, and Continuous Improvement.

🧘🏽‍♂️Soft Skills include: Communication, Collaboration, Lateral Thinking, Adaptability, Inspiration, Innovation, Empathy, Listening, Diplomacy, Organization, Responsibility, Leadership...

🎬🎥🎞 I really enjoy watching movies, and if I could redo it all over, I’d be a Sci-Fi script writer so I can be bounded by the limits of imagination.

(Opinions are my own)

Product Management, Media Automation

Owner of the AI vision and roadmap for Private Brand’s Compliance Division. Driving adoption of new technology, allowing my customers to scale their processes and increase productivity. Core responsibilities include:

-Building cross-functional teams consisting of Data Scientists, Software Engineers, Data Engineers, Product Managers and Program Managers, to plan and implement sophisticated AI projects
-Manage the New Initiative Intake Process
-Gather Business requirement artifacts
-Communicate roadmap definition and prioritization
-Coordinate User Acceptance Testing
-Manage Launch Announcements
-Prepare and communicate annual planning artifacts

Building Agentic AI systems beyond connecting APIs or LLMs is complicated, but not impossible.

This architecture lays the foundation for how AI agents think, communicate, and improve, covering everything from testing and observability to deployment and memory management.

Here’s a breakdown of the key layers and components that make up a scalable Agentic AI Architecture :

1.🔸Decomposition
Break down complex systems by domain (e.g., Coding Agent, Data Agent), by cognitive capability (Reasoning, Planning, Execution), or by agent role (Planner, Executor, Memory Manager, Communicator).

2.🔸Communication
Enable message passing between agents using inter-agent protocols or A2A (Agent-to-Agent) orchestration.
Support both single-agent and multi-agent setups for small or distributed workflows.

3.🔸Deployment
Deploy agents in containerized or serverless environments using Docker or Modal.
Support orchestrators like CrewAI or AutoGen for collective intelligence in multi-agent workflows.

4.🔸Data & Discovery
Integrate knowledge bases (like vector databases for RAG), memory stores (FAISS, Redis, Pinecone), and APIs for dynamic data access.
Context is passed using Model Context Protocol (MCP) for structured and real-time reasoning.

5.🔸Testing & Observability
Validate workflows end-to-end, test reasoning logic, and evaluate performance under real conditions.
Monitor using Weights & Biases, LangFuse, and track metrics like latency and task success rate.

6.🔸UI & Style
Provide intuitive feedback loops through visualization layers, dashboards, and self-reflective modes.
Enable collaborative, proactive, and goal-driven reasoning among multiple agents.

7.🔸Security
Protect access with token-based authorization and data encryption.
Include Trust Layers for human-in-the-loop validation and Policy Enforcement for safe execution.

8.🔸Cross-Cutting Concerns
Handle configuration, secrets, and environment management.
Support flexible frameworks like LangChain, AutoGen, or CrewAI for runtime execution and modular design.

Agentic AI is the future of automation - where AI doesn’t just assist but collaborates and learns.

Save this post to understand the architecture that powers the next generation of AI systems

#AgenticAI

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AI agents are great at working alone. However, they can also be effective when operating within networks of collaboration.

The real power of AI lies in how these agents communicate, coordinate, and complement each other’s abilities.

Orchestration patterns define this collaboration which turns isolated agents into intelligent, goal-driven systems capable of solving complex tasks together.

Here are 6 orchestration patterns that power the next generation of AI systems :

1.🔸Sequential Orchestration
Agents work one after another - each passing its result to the next.
Perfect for structured, step-by-step tasks like data pipelines or analysis chains.

2.🔸Concurrent Orchestration
Multiple agents work in parallel, handling different parts of a problem simultaneously.
This boosts speed, scalability, and overall task efficiency.

3.🔸Group Chat Orchestration
Agents communicate in a shared conversation, refining ideas together.
Useful for brainstorming, collaborative decision-making, or generating consensus.

4.🔸Maker–Checker Loops
One agent creates or proposes; another verifies, corrects, or approves.
Ideal for maintaining accuracy, compliance, and quality assurance in workflows.

5.🔸Handoff Orchestration
Agents transfer control dynamically based on context or expertise.
Best suited for adaptive workflows like support systems or multi-step automation.

6.🔸Magnet Orchestration
A central “manager” agent dynamically assigns tasks to the right specialists.
Enables fluid, context-aware collaboration across multiple intelligent agents.

AI Agents are evolving fast and orchestration is what makes them powerful.

Learning how these patterns can help you build smarter, more collaborative AI systems can be a huge advantage.

#AIAgent

AI Agent Orchestration Patterns

📚LLMs in the Enterprise are finally getting the playbook they deserve… LLMs in Enterprise by Ahmed Menshawy and Mahmoud Fahmy provides practical guides for building real AI systems that operate at scale.

Most teams talk about LLMs in theory. This book focuses on execution. It bridges foundational concepts with the hands-on design patterns that matter when you are integrating models into production environments.

Here are the insights that stood out👇

1.🔸Enterprise LLM integration is a data architecture problem
The book breaks down how to design pipelines, tune retrieval, and structure data so models operate with consistency and low latency in real workloads.

2.🔸Scaling LLMs requires pattern-level thinking
They go deep on architectural patterns that reduce complexity, improve efficiency, and streamline deployment. This includes RAG frameworks, fine-tuning strategies, segmentation techniques, and evaluation patterns that teams often overlook.

3.🔸Performance is not just about bigger models
The authors show how to optimize model behavior with advanced inferencing engines, contextual model customization, and monitoring systems that keep applications predictable.

4.🔸Real enterprise value comes from operational rigor
Security, fairness, transparency, and accountability are not afterthoughts. They are part of the design process, especially when LLMs touch business workflows and customer data.

5.🔸AI teams win by mastering both concepts and impact
The flow of the book reflects the real enterprise lifecycle: Concept → Customization → Impact
A clear, structured way to think about turning LLM capabilities into business outcomes.

If you are building production AI systems, leading an LLM program, or preparing for the next wave of enterprise adoption, you should definitely get a copy of this book.

Enterprise AI is evolving fast. Understanding these design patterns early puts at a career advantage which allows you to shape the next generation of intelligent applications.

#LLM

Check out this simple breakdown of the four core LLM architectures shaping most of today’s advanced systems.

Not all LLMs reason the same way. Building reliable AI systems, or choosing the right model for a workflow requires a clear mental model of the architectures that drive them.

Let’s review:

1.🔸Decoder Only (GPT, LLaMA, Grok)
Generates text by predicting one token at a time using previous context.

Best for generative workloads, conversational systems, and multi step reasoning.

2.🔸Encoder Only (BERT, RoBERTa)
Processes text with full bidirectional context to learn dense semantic representations.

Best for classification tasks, retrieval, and embedding generation.

3.🔸Encoder Decoder (T5, FLAN T5, BART)
Separates understanding from generation. The encoder builds meaning, the decoder transforms it.

Best for translation, summarization, and any structured text to text transformation.

4.🔸Mixture of Experts (Mixtral, Gemini 1.5)
Routes each input to a subset of specialized expert networks.

Best for scaling model capacity while controlling compute cost.

These architectures go beyond academic details to determine latency profiles, context handling, fine tuning strategies, and how models behave under real workloads. A clear grasp of them helps you design better pipelines, choose the right foundation models, and understand why two LLMs can produce very different outcomes on the same task.

Let me know your thoughts!

#LLM

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