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AI Thought Leader - Data Science & AI Technical Leader EMEA at IBM
Ranked in Top 10 Thought Leaders
Thought Leader in AI, Data Science, Quantum, Digital Transformation, Cloud, Agile, BlockChain, IoT
Author, Public Speaker, Moderator.
I am an internationally experienced, highly motivated professional and an executive data scientist who can design end-to-end innovative solutions for clients. I lead, plan and realise the strategy by actively contributing to it through thought leadership and my professional experience. I have about 13+ years of experience in design, implementation, and testing of systems using different languages, platforms, and technologies with a particular focus on ML, DL, Data Science, and Python. I am passionate about realising solutions using modern tech stack. I enjoy working with teams and leading them in agile way.
With an impeccable track record of well-honed communication skills, I cultivate relationships with stakeholders and motivate colleagues to implement complex solutions to deliver objectives effectively.
-Data Analytics, Data Science, AI, Intelligent Automation, Blockchain, IoT
-Digital Transformation, Data & AI Strategy
-Project and Program Management, Product Development Management, Risk and Change Management.
-Design, Delivery, and Implementation
-Agile and Design Thinking
Technologies and Methodologies:
Data Science, AI, Machine Learning, Cloud Computing, and Block Chain. ML, DL, Watson, AWS, Microsoft Azure, Hadoop, MapReduce, Spark, Kafka, Flume, Python, SPSS Modeler, Alteryx, Kylo, Ethereum, Solidity, HyperLedger, Mist, SQL and NoSQL-based technologies
Scaled Scrum, Scrum Master, Python
Available For: Authoring, Consulting, Influencing, Speaking Travels From: Munich Speaking Topics: AI, Quantum, Data Science, Blockchain, IoT, Industry 4.0, Digital Transformation etc
Points based upon Thinkers360 patent-pending algorithm.
Thought Leader Profile
AI Thought Leader - Data Science & AI Technical Leader EMEA
Company Type: Enterprise
Minimum Project Size: Undisclosed
Average Hourly Rate: Undisclosed
Number of Employees: Undisclosed
Company Founded Date: Undisclosed
Areas of Expertise
Autonomous Vehicles 30.19
Big Data 30.65
Business Strategy 33.63
Change Management 30.86
Design Thinking 30.94
Digital Disruption 33.06
Digital Transformation 30.62
Diversity and Inclusion 30.26
Emerging Technology 30.60
Future of Work 30.09
Health and Safety 30.22
Health and Wellness 30.09
Lean Startup 30.22
Open Innovation 30.11
Predictive Analytics 30.44
Public Relations 30.19
Quantum Computing 36.60
Agriculture & Mining
Federal & Public Sector
Financial Services & Banking
Higher Education & Research
Oil & Gas
Travel & Transportation
1 Adjunct Professor
Dr Chan Naseeb
September 21, 2017
Organise several lectures and workshops on Data Science and its applications
Augmented Intelligence Newsletter (AiN) # 4
January 04, 2022
For digital transformation, today, businesses depend heavily on analytics powered by artificial intelligence (AI) as a "must-have." Any data-driven business that ought to handle its processes with data as the salient glow can certify this. However, at the same time, many enterprises find it quite demanding to collect huge amounts of data and create sense of the data and apply it in the right context. As a result, they fail to get the most out of their growing information resources.
Becoming a Data scientist: which path to take?
August 04, 2021
Whether you have been into the data science field or just entering, I believe you will significantly benefit from this article in many ways. I will first outline the importance of the data science field, then we discuss different types of organizations, highlighting the importance of being data-driven. Then I will talk about the demand of data scientists, and their skills; emphasizing the dynamicity of the field and the need for continuous learning. Then I will dive deep into different routes available for you to become a data scientist or take your skills to the next level.
Understanding Quantum Computing, its potential, trends in 2021, and its applications
June 21, 2021
This blog will introduce you to Quantum Computing, followed by the business case for it. Finally, we touch base on its applications (a detailed blog on its applications will follow) and what to expect regarding advancements in this field.
Tags: AI, Digital Transformation, Quantum Computing
Moving from an intuitive mindset to data driven?
January 26, 2021
Recently I wrote an article on creating and living a data culture. You can find the first article, which defines what a data culture is? And enlists attributes of such a culture. In the sequel, I focused on those attributes and discussed those pillars of a data culture.
Understanding Artificial Intelligence, Machine Learning, Deep Learning and Data Science
November 20, 2020
In the article, I have tried to elaborate in very few words what do these terms such as AI, ML, DL and Data Science mean. Right from mimicing the human behavior to applying AI to get the value for the businesses. To read more on human and AI working together, read this article on collaborative intelligence.
Tags: AI, Digital Transformation, Business Strategy
AI and ML driving, exponentiating sustainable and quantifiable transformation
September 09, 2020
AI is a major transforming technology impacting every sector of life. AI is not a force to deprive humans and take over the control, rather a real enabler and lever for digital transformation. The former view aligns with Hollywood need to be undressed with the latter, which is realistic and becoming tangible over time as more organizations, and communities are leveraging AI’s potential. Developing a practical understanding of AI, its capabilities, the challenges, and opportunities that it brings is fundamental to get the maximum out of its envisaged potential.
The objective of this paper is to highlight how technology and industry have developed, discuss the role of AI in driving intelligent transformation concentrating on an applicable understanding of AI and related technologies. We introduce a new conceptual framework: AI's multi-dimensional role, to highlight its transformative power in multiple aspects and to support that with facts across different industry verticals.
Tags: AI, Digital Transformation, Digital Disruption
Activity Recognition for locomotion and transportation dataset using deep learning
September 01, 2020
Worked on a broad, real-life dataset to classify transport-related activities in a user and location-independent manner. Since deep learning architectures have now received great attention on achieving promising results on time series classification tasks, we focused our experiments on some recent state-of-the-art deep learning architectures such as CNN, Resnet, and InceptionTime. A considerable amount of time was spent on the preprocessing pipeline, which turned out to be a critical phase that impacted most of the results. At the end and after many experiments and hyperparameter tuning, we were able to achieve a 79% F1 score on the validation dataset using InceptionTime architecture. The objective of this paper is to present the technical description of the Machine Learning processing pipeline, the algorithms used, and the results achieved during the development/training phase.
Feature Store:A better way to implement Data Science and AI in and across your organization.
May 08, 2020
How could a feature store help in accelerating data and AI adoption across the enterprises?
Data Science and AI are great forces to transform your business and everything that you do, however, there is a huge possibility to optimise and automate data science and AI to leverage the fullest of their potential and capabilities. When organisation start their AI journey, they face many challenges and oftentimes it is needed but hard to accelerate its adoption.
World after Covid-19: A lot will change
April 21, 2020
The world of new opportunities, and new ways
I think we have changed, the work culture has changed and it will be changed. COVID-19 has changed our world and it will change more than what we had imagined.
While it might be hard for some to predict, for others to conceive, however for many it has surfaced already as they are experiencing it and living through it.
Tags: Culture, Digital Disruption, Health and Safety
Implications for the Data-Driven Businesses
April 17, 2020
What do businesses achieve, once they start their journey to become a data-driven enterprise? In this blog, I will discuss what you would obtain if you would become a Data-Driven business. Being data-driven is no longer a choice, rather it is a must-have if you want to stay relevant. Data is the new oil to explore the full potential of an organization and its capabilities. Once the organizations use it to their advantage, they can not only lead with data-driven decision making but also to deliver more digitally enhanced experiences to all the stakeholders and customers.
Tags: Big Data, Digital Transformation, Future of Work
Live your life… Just do it
April 14, 2020
Do not let anyone else take over to steer your life
Through reflection, you can avail the opportunity to appreciate, learn from and find peace with your past, while you take the conscious steps towards your future.
How would you define success?
This is your life, and you have the right to live it nobody else has the right to define the course of your life.
Who Can Become a Data Scientist?
April 11, 2020
What does it take to be a data scientist? Questions and Answers
an the following professionals (degree holders) become a data scientist?
Bachelor in Business Administration (BBA) holder
Here is my take on this type of questions:
Cognitive Enterprise: An End to End AI Strategy (to be Published)
July 31, 2020
In this book, I take the readers, the decision makers for a journey of how to transform their enterprises using AI. What are the critical decisions and choices they need to take and what are the steps to be implemented that will lead to a fully transformed and cognitive enterprise.
Developing high quality business strategies and plans ensuring their alignment with short-term and long-term objectives
Leading and motivating RemiNet team to advance employee engagement and develop a high performing managerial team
Overseeing all operations and business activities to ensure they produce the desired results and are consistent with the overall strategy and mission
Act within its powers as Company’s executive in-charge;
Set up and achieve the business targets;
and much more
Tags: AI, Digital Transformation, Quantum Computing
Building a case for Fair and bias-free AI
October 12, 2021
As we all know, Data science and AI can swiftly turn data into insights, and those insights can lead to decisions. And sometimes, the results are unconsciously spoiled by bias and drift, which can cause mistrust. This problem undoubtedly hampers AI adoption and can negatively impact people’s lives and a company’s reputation. Therefore it underscores the importance of following and applying the responsible computing approach. Such an approach should cater for at least making sure that the AI systems built are fair and bias free among other aspects. In this talk, I will set the stage for fairness and bias in AI, highlighting why it is needed to consider these aspects and how we should think about responsible computing in a broader perspective.
Thought Leadership on AI, Quantum Computing, Responsible Computing and Data Driven Innovation
December 13, 2021
You can watch more of my talks on different topics around AI, Responsible Computing (in the comments) and Data Science here https://www.youtube.com/channel/UCAbnQ5KV9pnz1sLoRvx9v_w. Quantum and more to come soon!
Tags: AI, Digital Transformation, Predictive Analytics
Augmented Intelligence Newsletter (AiN)
December 13, 2021
Welcome to Augmented Intelligence Newsletter (AiN) by C. Naseeb.
In this newsletter, I will talk about digital transformation, technology, science, and art - a little bit of everything. However, the key focus will be around tectonic forces (AI, Quantum Computing, Cloud, Blockchain, IoT, and their likes) that are shaping our society and its transformations. Some of the key points that will be considered are around these transformations, their impact on our society and lives, tech & trust etc.
Stay tuned and Happy reading (& learning which is the ultimate goal, reading is just a means to it)!
Tags: AI, Digital Transformation, Quantum Computing
Living a data culture
November 10, 2020
Being data-driven is critical to succeeding in today’s world. When an organization exercises a “data-driven” approach, it makes strategic choices based on data analysis and interpretation. Such an approach facilitates companies to experiment and organize their data with the goal of better serving their customers, employees, and improving their operations. Using data to drive its actions, an organization can contextualize and personalize its messaging to its prospects and customers for a more customer-centric strategy.
However, organizations face many challenges in becoming a data-driven organization and building and retaining a data-driven culture. Some of those challenges include their inability to emphasize long term objectives, lack of shared vision, focus on short term RoI gains and ignoring the Return on Value and opportunities that data brings, skills gap, and lack of having the full picture or true understanding of what it would mean for them if they become a DD organization.
nterprise success in a data-driven context depends upon complete access to data and instantaneous action, among other factors. To carry data to every decision, leaders need not only to tear down silos; rather, they have to manage and work with data where it lives strategically. Such a strategy should entail at least the following:
Identify the technological and cultural obstacles to realizing the full potential of data.
Leverage data organization while reducing friction.
Align their approach to data with overall business strategy.
Brainstorm and plan for data monetization opportunities
“Data-drivenness is about building tools, abilities, and, most crucially, a culture that acts on data.” Carl Anderson
Attributes of a business who has employed the data culture:
A Data-driven organization must display the following attributes:
All Decision making based on the data
Not being victims of their past success
Redesigning their strategy based on collaborative intelligence
Build a data culture
“Without data, you’re just another person with an opinion.” William Edwards Deming
Tags: Big Data, Change Management, Diversity and Inclusion
Path to Become a Data Scientist?
November 03, 2020
Whether you have been into the data science field or just entering, I believe you will significantly benefit from this article in many ways. We first outline the importance of the data science field, then we discuss different types of organizations, highlighting the importance of being data-driven. Then we talk about the demand of data scientists, and their skills; emphasizing the dynamicity of the field and the need for continuous learning. Then we dive deep into different routes available for you to become a data scientist or take your skills to the next level.
You must have heard about the fact that Data Science is the sexiest job of the 21st Century and might be wondering how come, it is so overhyped? Why is everyone talking about it? Even after the pandemic, people started to talk more about it and use data science techniques to analyze data. Some got it right, and some got it wrong; that is any way out of scope to our discussion here.
Undoubtedly, data and AI is the fastest growing industry with multi-billion dollar potential. Consequently, every organization is trying to make the most out of it. There are three types of organizations.
1) which have the data and they would like to get insights out of it,
2) the ones who have the skills, and can gain insights and help businesses become data-driven. The ones which are providing data science skills, experts, and consultancy services,
3) the ones who provide specialized platforms to support the organizations in achieving their data-related objectives.
You may see some organizations having a blend of these skills together. However, these are the capabilities that businesses need to become data-driven. Considering the costs and associated ups and downs, some companies may develop their capabilities or outsource them.
Different types of organizations and their dire wish to become data-driven dictates the high and urgent demand for data scientists and Machine Learning engineers. They are the ones, which would in the end crunch the numbers and make sense of them and uncover hidden insights in the data for the businesses.
With all the exciting and wide range of opportunities being available for data scientists, getting yourself skilled and becoming acquainted with data science is a great way to show your competitive edge and prove your value for the business. Data Science is a very complex, dynamic, and continuously evolving field, which makes it challenging as well as exciting. The skills needed, languages that you can leverage to build data science and machine learning pipelines, libraries, frameworks, tools are constantly changing and maturing — this asks for nothing more, nothing less than continuous learning.
Let’s discuss the different options and paths available for you to become a data scientist and (if you are already a data scientist) what to do more to achieve the next level of expertise.
There are several universities providing degrees (Master Level) or postgraduate level courses in data science under different names such as data analytics, machine learning, data science, business analytics, to name a few. There are also various online (remote) alternatives available. One has to evaluate them according to their circumstances, suitability, and affordability. It is up to you to choose a college or an online, in the case of online studies, you do not have to relocate, but costs might still be high, depending on the program that you choose.
2. Data Science Fellowship Programs
There are many institutes, companies, or startups offering several months hands-on fellowships, which give you the possibility to do hands-on assignments focusing on solving a business problem for one of the partners (or sponsor)of the institute/fellowship. It is an excellent opportunity for both the business and fellows to see if they fit for each other in the longer run, and in short-run companies get their problem solved while you as a graduate get the hands-on experience for a real project.
3. Learning through online platforms (MOOCs)
Many online sites are offering courses that you can take to become an expert in the art of data science. I call it art and science because it involves both of them; solving business problems is an art, and solving it using data science, as its name suggests, is science. And you need both; only one would not suffice in today’s job market.
There are many online platforms offering courses in data science; some of the providers include Coursera, Edx, DataCamp, Udacity, Udemy, and many more. Also, many universities are offering online courses and specializations through these platforms. These programs are economically feasible for pretty much everyone, and you can take them at your own pace, which helps the student to grasp the concept and still do the whole course. It is up to you to decide for an individual course, a micromaster or a nano degree program
In addition, there are a few hands-on projects, which help you to see a step by step guideline on how to build a project for a business problem.
4. Doing hands-on projects
This centres around taking it on your own, taking the control in your own hands, and steering it yourself. Although this is true for all other cases but this is more true and much needed in this case. You have to start with one hands-on project and scale it up and sideways for application scenarios, algorithms, etc.
5. Reading books
Especially for those who love reading, to learn a concept, and then implement or master it. It is also an excellent way for many, but maybe not for all; It takes time, and you might be lost when it comes to doing hands-on. I would recommend this only once you have the necessary hands-on experience and know the fundamentals. And in today’s agile and fast-moving world, I would emphasize to start with any source that you like and combine it with others as you go along. There is no one silver bullet.
There are a plethora of articles on almost every subject on Medium. They are usually not very long, and this helps in many ways to keep you focused, to get going by achieving valuable and useful knowledge, and get it implemented and turn it into something that sticks. To be able to use this platform, you can either create a free account or a paid/member account. Then the next step is to look for articles in top publications such as TowardsDataScience and TowardsAI. I usually publish my articles in these publications.
One great way to learn Machine Learning (and anything else, of course) is to write about what you have learned. You could also become a writer on Medium, and submit your stories about Data and AI to this publication here.
7. Kaggle and other competitions
Many people nail down technical concepts by joining competitions like Kaggle and others. It is also a great way to put yourself into a framework of discipline, following the deadlines, being challenged, and achieve something which, in addition to you mastering on concepts, gives you monetary benefits, gets you the recognition that you can claim for your fame. Of course, It does not happen overnight, it takes time, and everything does, so be patient and persistent in whatever you do.
8. Youtube Videos
You can also learn data science by following on YouTube. There are many courses or subject specific, both short and long videos that you can quickly serach and skim through.
Overall you are the one who knows yourself the best, what works for you, and what does not? You know the things about you which you might be reluctant to share with others but would help you to decide which option (or combination of them) would suit you the best. Let me raise some questions here, which might help you to think, plan, and execute your journey to become a data scientist.
Should I consider an online or face to face program?
What are the advantages and disadvantages for each of them in consideration with my particular scenario, life situation, monetary and other aspects?
Can I combine them with my current job, or shall I take a break and join an onsite fellowship?
What are the concepts that I know well, and what do I need to master, or should I start from scratch?
Are there any pre-requisites? Do I need to learn a programming language first, for example?
What is the time commitment required?
What is the required cost contribution on my behalf?
Think about these questions and others and try to answer them for yourself to evaluate the best way for you to learn and master data science.
2021 Predictions for Quantum Computing
October 08, 2020
[Image by Gerd Altmann from Pixabay]
I think 2021 will be the year when QC becomes more democratized and goes mainstream. While technologies like IoT, hybrid cloud, and data analytics — will still strongly have their position in the business landscape —, I see an expanding usage of AI, fifth-gen networks (5G), and above all, quantum computing. Besides its applications in cybersecurity, drug development, financial modeling, better batteries, traffic optimization, climate change, materials discovery, simulation, AI, etc., it can take our understanding of nature and chemistry to a level that has never been feasible before.
I expect the following quantum trends to accelerate in 2021.
1. Quantum Machine Learning- AI and Machine Learning. As AI has seen many applications already, it is the next realistic step to leverage quantum machine learning for easy and realistic QC usage.
2. Covid-19 and Beyond: QC could play a pivotal role in use cases like vaccine development and identifying and managing the spread of viruses. Potential applications of QC include predicting the evolution of COVID-19 using quantum machine learning. Quantum sensors, including NV-diamond sensors, have the potential to detect COVID-19. Quantum plasmonic sensors with considerably less noise could be utilized in blood protein analysis, chemical detection, and atmospheric sensing.
3. Quantum Algorithms: The development of new Quantum Algorithms and conversion from classical algorithms into quantumized algorithms.
4. Performance improvement and Optimization of Quantum Noise.
5. Development of new (or advancements in) Real-Time architectures for QC.
6. Use Cases: Expanding Industrial Use Cases suitable for Quantum Computing.
7. Conventional cum Quantum: Hybrid computing approach to problem-solving. This new paradigm of computation with a hybrid approach will result in the emergence of novel ways to solve the existing business problems and bring new opportunities, which were inconceivable earlier.
8. Adoption: Quantum computing enterprise adoption at scale, dependent on advances in storage, integrations, deployments, and security of quantum applications.
9. Advances in Quantum cryptography and Quantum-safe cryptography
10. QCaaS: QC as a Service will be a natural choice for organizations to tap into the experiments.
As we move forward with ongoing research and innovation in Quantum technology, the need for products and platforms that support diverse algorithms, frameworks, and hardware with unified development and deployment experience to the developers will start gaining traction. We saw some initial efforts in this area in the 2019–20 that will get a further impulse in 2021. Though the adoption of Quantum at scale might seem to be a faraway reality, the work in the next 1–2 years will determine the speed at which industry will start strategizing, building, and deploying quantum applications.