81k | Director, Data & Analytics/ AI @ Gartner

Summary

Making state-of-the-art Artificial Intelligence concepts accessible and profitable to professionals from any background for over 35 years!

Highlights
-  Veteran innovator with 3 decades of experience leading state-of-the-art AI and Analytics ventures
-  98% of students surveyed would recommend me to peers
-  Generative AI/ Agentic AI expert-  ChatGPT, Claude, Copilot, Perplexity, Lovable, n8n, more
-  Experienced AI instructor for Cisco, LinkedIn Learning, University of Pennsylvania, & Simplilearn
-  Senior AI Curriculum Advisor for Graduate School of Education at Ivy League university UPenn
-  Winner, International Society of Data Scientists Outstanding Voice Award
-  Social media leader with over 107k LinkedIn followers


Selected Clients
 
-  3M
-  Audi
-  Biogen 
-  Booz Allen Hamilton
-  Bristol-Myers Squibb
-  Cisco
-  Citicorp
-  Franklin Templeton
-  Gartner/ Evanta
-  Harvard Innovation Labs’ Experfy
-  IBM
-  International Olympic Committee
-  Los Angeles Times
-  Mercedes
-  Morgan Stanley
-  Royal Bank of Canada
-  St. Jude’s Children’s Hospital
-  Wells Fargo
-  US Homeland Security
-  US Veterans Administration 
-  Yale University

Partial Skills Inventory
 
-  AIOps
-  Accountable AI/ Ethics
-  Anomaly Detection
-  Anthropic/ Claude
-  Artificial Intelligence
-  AWS
-  Brokerage Firms
-  Client Management
-  Consulting
-  Compelling Presentations
-  Compliance Regs and Frameworks
-  Curriculum Development
-  Dashboards
-  Data Storytelling
-  Data Visualization
-  Education
-  Explainable AI
-  Feature Engineering
-  Financial Services
-  Fraud Detection
-  Gap Analysis
-  Generative AI
-  GitHub Models
-  Google Cloud
-  Google Gemini
-  Insurance
-  KPI's and Metrics
-  Lovable AI
-  Machine Learning
-  Microsoft Azure
-  MLOps
-  Model Ensembles
-  Nano Banana
-  OpenAI/ ChatGPT
-  Orange Data Mining
-  Perplexity
-  Postgres SQL
-  Professional Development
-  Research
-  Retail
-  Retention and Attrition
-  SPSS
-  SQL Server
-  Underwriting

T. Scott Clendaniel

🔬 #ArtificialIntelligence 𝗛𝗶𝗿𝗶𝗻𝗴- 𝗝𝘂𝘀𝘁 𝗪𝗵𝗮𝘁 𝗔𝗥𝗘 𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗿𝘀 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗙𝗼𝗿?

The Terrible Facts:
- Artificial Intelligence needs a lot more experts.
- Tons of qualified professionals aren't getting hired.
- The expectations of hiring managers are just out of control.

If you're a hiring manager, please STOP looking for an entire department in just one employee.  It simply doesn't work that way.  You need a team.

Personally, I find the job search to be truly dreadful. It is exhausting, soul-crushing, depressing, dispiriting- actually, I will run out time before I run of negative emotions on this topic!  

But I hold out hope that one day everyone else will figure this out!

Please follow me for more analysis! 

- T. Scott Clendaniel

#AI Training for Executives | 108K LinkedIn Followers

It’s been a while since I started my role at Nautilus-AI.com as a Chief Learning Officer, but I wanted to share this update with everyone.

🔬 #ArtificialIntelligence: 𝗪𝗵𝗲𝗿𝗲 𝗗𝗼 𝗬𝗢𝗨 "𝗔𝗜"?

Our friends at GiGadgets researched something extraordinarily fun!

AI isn't the only place where innovation occurs.  Sometimes, the very desks we use to work with GenAI, Machine Learning, or any of the rest of AI occurs can show us remarkable creativity.

Take a look and smile!

- T. Scott Clendaniel

🔬 True Wisdom via Michael Tchuindjang!

Cybersecurity Thought Leader | Bridging Academia & Industry | Mentor for Startups & Aspiring Cyber Pros | Board Member Driving Cyber Impact | Associate Lecturer in Cybersecurity

Boardroom AI Strategy Meeting (2026 Edition) 



CEO: Who are we?
Boardroom: CEOs

CEO: What do we want?
Boardroom: AI!

CEO: AI to do what exactly?
Boardroom: We don’t know

CEO: When do we want it?
Boardroom: RIGHT NOW

If you’ve ever sat in a boardroom lately, this probably feels uncomfortably familiar.

AI has become the new magic word:

Replace “digital transformation”

Upgrade from “blockchain”

Stronger than “cloud-first”

Don’t get me wrong, AI is here to stay.

But AI without a clear problem, data readiness, risk assessment, or ethics is just… expensive enthusiasm.

Real innovation starts when we ask better questions:

What problem are we solving?
What risks are we introducing?
Who is accountable when AI fails?
Are humans still in the loop?

Otherwise, we’re not building AI strategies, we’re just speed-running confusion with a GPU 😄

Credits to @Cyber Writes ✍.

#ai #cybersecurity #security #leadership #aihype #responsibleai #jobs #hiringandpromotion #hiring #techhumor #humor #memes #klex

🎉 𝗚𝗲𝗻𝗔𝗜 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 𝗪𝗶𝘁𝗵 𝗧𝗵𝗲 𝗣𝗲𝗿𝗶𝗼𝗱𝗶𝗰 𝗧𝗮𝗯𝗹𝗲! 

Here is the magic of video!

Generative AI can build entire new worlds.  This is one I absolutely love.  Mini planets made from elements in the period table:


𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀

▶ 1.  Copper
▶ 2.  Mercury
▶ 3.  Calcium

▶ 4.  Magnesium
▶ 5.  Palladium
▶ 6.  Uranium

▶ 7.  Arsen
▶ 8.  Lead
▶ 9.  Chlorine

Happy Chemistry

Please SAVE and SHARE!  You can follow me: T. Scott Clendaniel

🔬 𝗧𝗲𝗮𝗰𝗵𝗶𝗻𝗴 #AI: 𝗦𝘁𝗲𝗮𝗹𝗶𝗻𝗴 𝗮 𝗩𝗶𝗱𝗲𝗼 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗳𝗿𝗼𝗺 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝘆!

Picture this: you’re holding a cube. It’s got 8 corners, 12 edges, and 6 faces. 

Add the corners and faces, subtract the edges, and you always get 2. Every time. That’s Euler’s Polyhedron Formula—V - E + F = 2. It works for a cube, a pyramid, even a soccer ball.

So why does that matter? Because it’s a reminder that under all complexity, there’s structure. Euler didn’t just find a number—he found a pattern that connects everything.

That’s how I think about explaining AI. The goal isn’t to drown people in details; it’s to show the structure beneath the surface. When you connect data (edges), decisions (vertices), and outcomes (faces) just right, people get it.

The best teachers—whether they’re explaining geometry or generative AI—make the complex feel simple and the simple feel profound. That’s how real understanding sticks.

Happy Learning,

T. Scott Clendaniel

🔬 #ArtificialIntelligence Innovation: Human-Powered Helicopter!  

Human-powered helicopter hovers for more than a minute, defying sceptics' claims it was scientifically impossible!


𝗧𝗵𝗲 𝗖𝗼𝗻𝘁𝗲𝘀𝘁

The Igor I Sikorsky Human Powered Helicopter Competition was launched in 1980 by the American Helicopter Society (AHS). The rules stated that the $250,000 prize could only be won by a human-powered machine capable of hovering for at least 60 seconds, reach at least 3 metres in height, and stay within an area 10 metres by 10 metres.

On Thursday, the award was handed over to the AeroVelo project, led by Todd Reichert and Cameron Robertson. Their winning flying machine, called Atlas, has a set of 20-metre rotors at each corner of a square frame 50 metres on each side, and is powered by a bicycle at the centre of the square.

Thanks to the use of lightweight materials, such as carbon fibre, the whole setup only weighs only 55kg despite its huge size. The team describe the Atlas machine in detail in a claim for a world record to the FAI (Fédération Aéronautique Internationale).

"This isn't something that you're going to commute to work in any time soon, but it's an exercise in really pushing the limits on what's physically possible, and what you can do with lightweight materials and really creative design," Reichert told the Canadian Press. "Winning this competition really is a catalyst to keep doing the things we love. Our goal is to take on projects that really inspire people to follow big dreams."


𝗧𝗵𝗲 𝗪𝗶𝗻𝗻𝗲𝗿𝘀

The AeroVelo team started work on Atlas in January 2012 and flight testing began in August. They refined the design over the following months and, despite two big crashes that destroyed much of their work, Reichert piloted the machine to make the record-breaking flight on 13 June (if you're reading on a mobile device click here to see the flight).

At the presentation of the prize on Thursday, AHS executive director, Mike Hirschberg, said that many in the world's vertical flight community had thought the Sikorsky challenge was impossible. "Several studies 'proved' that it was in fact scientifically impossible," he said. "A human being simply could not generate enough power for long enough to keep a human-powered helicopter in the air for 60 seconds, much less reach 3 metres. And when you add the weight of a control system to keep it hovering over a 10-metre box, it made it even more impossible.

"Well, it took a third of a century to prove those sceptics wrong. It took that long for the state of the art of vertical flight to see significant technological advances in lightweight structures, computer-aided design, aeromechanics, and multidisciplinary design optimisation."

Gotta love innovation!

T. Scott Clendaniel

🔬 #ArtificialIntelligence 𝗶𝗻 𝗗𝗲𝘀𝗶𝗴𝗻: 𝗣𝗿𝗼𝘀𝘁𝗵𝗲𝘁𝗶𝗰 𝗟𝗶𝗺𝗯𝘀!

SoftFoot Pro is a cutting-edge, motorless prosthetic foot developed by Italian researchers, mimicking human foot anatomy with flexible chains and titanium to adapt passively to terrain, offering better stability, natural movement (like bending), and improved energy efficiency for amputees, and also serving as a robotic component. 


---
𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 & 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀
---

▶▶ 𝗛𝘂𝗺𝗮𝗻-𝗜𝗻𝘀𝗽𝗶𝗿𝗲𝗱 𝗗𝗲𝘀𝗶𝗴𝗻
Emulates the shape and structures of the human foot, including the plantar fascia, using flexible components.

▶▶ 𝗣𝗮𝘀𝘀𝗶𝘃𝗲 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻
Its flexible sole and chain system allow it to deform and widen its contact area, naturally adapting to rough or slippery surfaces without motors.

▶▶ 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗠𝗼𝗯𝗶𝗹𝗶𝘁𝘆
Enhances stability, allows for natural actions like bending down, and makes navigating stairs easier for users.

▶▶ 𝗟𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁 & 𝗦𝘁𝗿𝗼𝗻𝗴
Weighs about 450g and supports up to 100kg, using titanium/aluminum and high-strength plastic chains.

Have a great week!

Follow me for more tips: T. Scott Clendaniel

🔬  #GenAI- 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀!

CNN's are the basis for many tools, including most of the computer vision, image, and video generation for Generative AI.  Here's how to teach it to your team.


𝗧𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀

A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio.

Convolution-based networks are the de-facto standard in deep learning-based approaches to computer vision and image processing, and have only recently have been replaced -- in some cases -- by newer deep learning architectures such as the transformer. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections.

For example, for each neuron in the fully-connected layer, 10,000 weights would be required for processing an image sized 100 × 100 pixels. However, applying cascaded convolution (or cross-correlation) kernels, only 25 neurons are required to process 5x5-sized tiles. Higher-layer features are extracted from wider context windows, compared to lower-layer features.


𝗦𝗼𝗺𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗖𝗡𝗡'𝘀

▶▶   image and video recognition
▶▶   recommender systems
▶▶   image classification
▶▶   image segmentation

▶▶   medical image analysis
▶▶   natural language processing
▶▶   brain–computer interfaces
▶▶   financial time series

CNNs are also known as shift invariant or space invariant artificial neural networks, based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps.  Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input.

Via Wikipedia ==> https://bit.ly/4126GDT

Happy Learning,

T. Scott Clendaniel

🔬 "𝗜𝗳 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗜𝘀 𝗕𝗼𝗿𝗶𝗻𝗴, 𝗜𝘁'𝘀 𝗕𝗲𝗶𝗻𝗴 𝗧𝗮𝘂𝗴𝗵𝘁 𝗕𝘆 𝗧𝗵𝗲 𝗪𝗿𝗼𝗻𝗴 𝗣𝗲𝗿𝘀𝗼𝗻!"

Okay, so Richard Feynman wrote the original quote about Science in general, not about Artificial Intelligence specifically.  So I confess.  I took a wee bit of dramatic license!

But UNILAD captured this fantastic video of Tatiana Erukhimova in here element teaching Physics.

And quite frankly, AI has a lot more possibilities for interactive content!

So no excuses!

And if you need help, reach out to me directly!  🤗 

- T. Scott Clendaniel




🎥 credits UNILAD/ Texas A&M University

🔬 #AI #Education:
Introducing 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 demo- with 𝗣𝗨𝗣𝗣𝗜𝗘𝗦! 🐶🐶🐶

In the world of AI, Reinforcement Learning is truly a star, and the basis of things like self-driving cars/ autonomous vehicles.

It contains a few elements:

▶  Problem to be solved
▶  An agent, who is observed trying to solve the problem
▶  Reward/ penalty functions
▶  Scoring system

Just like these adorable puppies, who serve as our agents, trying to learn how to reach the treats, our agents try to figure out the problem.  Each puppy's success or failure is recorded, and the reinforcement learning system eventually solves the puzzle!

Enjoy!

T. Scott Clendaniel

🔬 𝗠𝗮𝗸𝗲 𝗬𝗼𝘂𝗿 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗖𝗼𝗺𝗲 𝗔𝗹𝗶𝘃𝗲!

The art of surprise will help your learners stay intrigued as you go through your training!


𝗖𝗮𝗻 𝗬𝗢𝗨 𝗳𝗶𝗴𝘂𝗿𝗲 𝗼𝘂𝘁 𝘁𝗵𝗲 𝘀𝘂𝗿𝗽𝗿𝗶𝘀𝗲 𝗕𝗘𝗙𝗢𝗥𝗘 𝘆𝗼𝘂 𝗰𝗹𝗶𝗰𝗸 𝗽𝗹𝗮𝘆?  Tell me in the comments below!

Please make sure to #SAVE and #REPOST so your followers can take advantage of the advice!

- T. Scott Clendaniel

Motorola Mobility (a Lenovo Company)

Deriving Dollars from Data by designing, developing and deploying machine learning algorithms focused on CRM and Marketing

Data Science Manager

As a lead data scientist partnered with functional teams for enhancing business using Data Science & RPA. Led a team of 3 data scientists in the Advanced Analytics Team.

Improving Product Mix: Analyzed millions of customer orders to find out similarity of product configurations and enabling the design of new “Build-To-Order” products resulting in margin gain of $1 million for Indian Market. Enabled the scaling of the algorithm for WW markets.

Predicting Deal Consumption: Predictive Model to estimate the proportion of consumption related to a deal with >80% accuracy to aid the discount offering process. Simplified the use of model results for the end-user with an API integrated to MS Excel.

Price Range Recommendation: Analyzed thousands of historical selling price points of different products to arrive at a suitable price range for each product using Monte-Carlo Simulation thereby easing the price negotiation task for sales team. Intangible benefits comprised of reduction in TAT and >30% reduction in Level-2 Approvals.

Improving Customer Catalog: Analyzed the purchase patterns of key accounts through customized catalog and the open market route and using Association Rule Mining, identified the various products that needs to be included/offered through the catalog.

Deal Win Probability: Developed a Predictive Model with >75% accuracy to estimate the probability of winning a deal thereby helping the sales team in pursuing the deal

Lead Data Scientist - Advanced Analytics

• Successful implementation of RPA in more than 15 processes with ~25 head-count reduction and savings in man-days.
• Identified & evaluated more than 100 processes across different functions in the organization (Finance, Sales Ops, HR) for suitability of automation based on complexity, dependency, stability etc.
• Process Mapping and Documentation at Click-Level with all possible What-If Scenarios thereby easing the Technical Solution Architecture and Bot Configuration Process
• Collaborated with TSA/Developer and the business team in organizing and evaluating test cases.

Business Analyst - Robotics Process Automation (RPA)

Juniper Networks

Supported the Marketing Vertical of Juniper Networks by developing Statistical Models enabling marketing decisions and enhance sales. Developed predictive models to identify prospective purchasers of technology products, customer segmentation and an interactive analytical solution for Cross-Selling technology products.

Data Scientist

Hewlett-Packard

As a key member of the "Advanced Analytics"​ team, developed predictive and forecasting models for Sales Compensation Program resulting in optimizing the quota set for sales reps. Further improvement in the accuracy (~94%) of predictive model using Support Vector Machines (SVM)
Co-developed two new machine learning algorithms based on the Gaussian distribution to detect erroneous payments in the invoice processing units of HP with an initial recovery of US$1Million and a further potential of recovery up to US$5Million.
Involved in developing a decision tree based model to predict the probability of winning a deal. 
As a key member of the newly formed "Data Labs" team, responsible for developing different statistical/analytical models supporting Healthcare, Manufacturing and Telecom verticals.

Bank of America

Handling Analytics and Business Intelligence projects related to various financial sectors like Payment Processing, Home Loans, Small Business etc by applying basic and advanced statistical methods to churn out information for decision making

Manager

Unisys

Driving Global Customer Satisfaction programme under the Project Quality Office/Project Management Office.
Churning information from csat data/feedback using statistical techniques.
Publishing metric dashboards.

Programme Manager

Wipro

Working in the Corporate Quality Division as a senior executive, was instrumental in some of the key projects resulting in big business benefits for the organization:
- Developed a predictive model for a popular consumer care product which resulted in zero rejections and an annual savings of more than Rs.1 Crore
- Provided statistical analysis and inputs for the HR functions in development of SLAs for hiring cycle time based on skill-sets resulting in timely hiring of candidates and paved way for creating a taxonomy of skill sets
- Identified the problematic steps of recruitment hiring and initiated SLAs for each step in the process thereby reducing the hiring cycle time and also reducing the offer letter rejections by candidates
-Developed a predictive model based on survival analysis to predict the length of stay of an employee in the organization
-Developed a predictive model for a lighting product to estimate the warranty that can be offered

Senior Executive

Gallup

Data Analyst

IMRB International

Asst Executive - Field Ops

Statistician | Data Science Manager | ASQ CSSBB

Data Scientist and a Certified Six Sigma Black Belt (ASQ) with 15+ years of experience in applying statistical techniques for solving business problems across industry verticals. Rich experience in applying six sigma methodology and quality improvement. Focus on new developments in analytics and data mining. Development and Implementation of data mining and machine learning techniques/algorithms in different domains. Hands-on experience in the use of R and KNIME.

Specialties: Statistics, Data Science, Quality, Analytics, Six Sigma

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Thejasvi TV

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