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Dr. Chan Naseeb | Data Science & AI Leader |IBM

Dr. Chan Naseeb: Navigating Data Science Frontiers and Charting AI Pathways to Help the World Develop and Advance Through Challenging Times and Put Sustainable Initiatives at the Front

Imagine a world where innovation thrives, where creators join forces to shape a future empowered by technology and expertise. In this landscape, Dr. Chan Naseeb stands at the forefront of Data Science and AI leadership in the realm of IT Services, Consulting, and Enterprise Transformations. He is a luminary within a prominent company-IBM driving transformation for enterprises worldwide.

IBM, an enterprise that goes beyond mere work, is a realm of creation. It’s a hub where technologists, developers, engineers, designers, business leaders, and others unite to craft solutions that transcend boundaries. Collaboration knows no bounds here–it extends to partners and even competitors.

Dr. Chan’s remarkable journey intertwines with this ethos. As a leader in Data Science and AI and other advanced technologies like Quantum computing, he orchestrates the orchestration of the unseen, guiding his team, clients, and partners toward the uncharted realms of possibilities. It’s a role that extends beyond the present, delving into the ‘what if‘ scenarios that shape the destiny of industries. In this domain, ‘creating‘ takes on a profound significance. It’s not just about software or systems; it’s about shaping the contours of tomorrow. It’s leveraging AI and other tectonic technologies to transform enterprises while focusing on AI augmenting human intelligence.

Dr. Chan is an Executive Leader and AI ethics Evangelist with experience in strategically empowering enterprises with AI and Data Science. Highly effective strategist, thought leader, business & product development leader, delivery leader, data science and AI practitioner with almost two decades of experience transforming businesses, emphasizing innovation and creativity in solving complex problems and building end-to-end solutions. He is an energetic and results-focused Data Science & AI leader with success spanning more than a decade-long experience in leading diverse, distributed, and large teams to achieve outstanding results.

He has a proven track record of transforming businesses via data science & AI, information technology, and process automation. Highly experienced at leading via influence across complex corporate organizations, as demonstrated by having built, developed, and executed corporate-wide strategies.

A consistent theme throughout his career has been to combine existing technologies to create industrial-scale processes, including innovative automation, IT systems, and analysis pipelines to support these. He is an adept leader of science, business, analytics, and technical teams with a state-of-the-art understanding of contemporary technologies and the appropriate application of these technologies to crack problems at a massive scale. His desire for continuous learning, growth, and development keeps his approaches, skills, and leadership relevant. He has strong leadership skills in leading large, diverse, geographically dispersed teams.

Let’s delve into Dr. Chan’s journey; a testament to the power of collaboration, innovation and above all, the art of creation!

Can you provide an overview of your background and experience in data science and AI that led you to pursue a career in the sector?

My passion for AI and data science ignited over two decades ago with my first neural network implementation. Fueled     by my love for Mathematics, Statistics, and modeling, I delved deeper as Deep Learning emerged. My journey led to a Data Science Ph.D., launching a career crafting solutions for global clients in various sectors like Finance, Healthcare, Oil & Gas, Retail, and many more. I’ve mastered AI Strategy, crafting the roadmaps, and realizing their implementations. I worked across the AI lifecycle, crafting models across areas like Deep Learning, Natural Language Processing, Conversational AI, Computer Vision, and Generative AI in numerous domains and countries while focusing on Trustworthy AI and Human-Centered AI.

A couple of reasons why I choose to have my career in data science and AI include my readiness to get out of my comfort zone, accept new challenges, and learn new ways to accomplish tasks, the Big Data Movement, and the love of AI / Neural networks, being curious, love for models, mathematics, science, their business implications and applications, and keeping myself ahead of the competition.

In your opinion, what are the key skills and qualities that a successful data scientist and AI professional should possess?

Among the numerous skills required, both technical and non-technical, I emphasize focusing on non-technical skills as they’re paramount. Technical skills can be acquired over time. Non-technical skills encompass:

  • Intellectual Curiosity: Cultivate a thirst for novel problem-solving approaches.
  • Attitude: Maintain a positive outlook; it can conquer seemingly insurmountable problems.
  • Willingness to Learn: Be open to acquiring new skills. The dynamic nature of AI and IT mandates continuous learning.
  • High level of drive and initiative: Willing to go the extra mile and out of the box thinking.
  • Ability to navigate ambiguity at ease: Rigorous and solution-oriented problem-solving and analytical skills, combined with the capability of thinking through nuanced and complex situations. Throughout my journey, I’ve encountered the ever-evolving nature of AI and IT, particularly in areas like Generative AI. Even a brief hiatus highlighted the rapid advancements. Adaptation is crucial, irrespective of your role, to stay abreast of this swiftly changing landscape.

How do you approach leadership and what values do you prioritize in your work?

Leadership transcends titles or positions; it’s an inherent trait reflected in daily actions. Its essence begins within oneself, extending to family, friends, and work. Leadership emanates through thoughts, deeds, values, outcomes, and influence. It involves ownership beyond assigned roles, embracing risk, courage, and effort. Prioritizing self-improvement drives my approach, a practice I advocate for global positivity. Embracing humanity, vulnerability, curiosity, and responsible action defines leadership’s core.

How do you stay up to date with the latest advancements and trends in data science and AI?

I employ various strategies for growth. Regular reading of books, newsletters, scientific papers, and tech updates keeps me informed and helps leverage advancements for enhanced impact in my work.

I’m indebted to my employers, colleagues, clients, partners, and friends. They constitute my power base, triggering valuable discussions and insightful moments that foster my growth. To each of them, I extend profound gratitude. I urge them to persist in their support, as their contributions are immeasurable and deeply appreciated.

Collaboration is often crucial in data science projects. How do you foster collaboration and effective teamwork within your team or across teams?

Indeed, data science embodies a team effort. I’ve witnessed this firsthand, leading and being a part of teams across diverse locations, including in the face of the pandemic. The crux lies in collaborative efforts. Remarkable outcomes stem from cohesive teams.

In my perspective, successful teamwork entails openness, vulnerability and shared responsibility. Such principles foster dynamic collaboration and bring my philosophy for effective teamwork to fruition.

As a leader, how do you foster innovation and encourage creativity within your team?

It commences by challenging existing methods and pondering alternative approaches. I facilitate this by posing pertinent questions and stimulating my colleagues’ thought processes. Moreover, I inspire them to embrace risk, step beyond their comfort zones and explore inventive paths that might otherwise remain uncharted. Surprisingly, I often glean fresh insights from my team members.

Furthermore, collaborating with other teams introduces novel viewpoints, igniting creativity and innovation. This multi-faceted approach contributes to our collective growth and progress.

What strategies do you employ to drive growth and success in your organization?

I employ several strategies to drive growth and success

  • Shared Success: I believe success is a collective journey that multiplies when shared.
  • Embracing Growth: I advocate stepping beyond comfort zones and embracing vulnerability as a means to grow. Failures, when accompanied by lessons, are invaluable.
  • OKRs for Growth: I guide my team to include these principles in their Objectives and Key Results (OKRs) to encourage stepping out of comfort zones and witnessing tangible growth.
  • Pairing for Learning: Collaborative efforts accelerate learning; pairing up individuals promotes rapid knowledge transfer.
  • Goal Setting: I adhere to the concept that written goals enhance achievement odds. We outline goals, set standards for measuring them, and update them throughout the year, reflecting our commitment to continuous learning.

These strategies collectively foster a culture of growth, learning and shared accomplishment.

Can you share any advice or tips for aspiring data scientists and AI professionals who are starting their careers in this field?

Here are some key strategies for growth and success:

  • Agile Objectives: Set clear, agile objectives and establish SMART goals for skill acquisition.
  • Hands-On Learning: Keep yourself up to date and gain knowledge through hands-on projects that you can showcase potential opportunities.
  • Business Impact: Emphasize the business impact of your solutions, aligning outcomes with key business indicators rather than overwhelming them with technical jargon.
  • Team Collaboration: is essential for success.
  • Leverage Existing Resources: Build upon existing solutions and knowledge rather than reinventing the wheel.

How do you see the future of data science and AI evolving, and what opportunities and challenges do you anticipate?

The future of data science and AI is promising and transformative, reshaping our world. The advent of Generative AI, Foundation Models, and Large Language Models (LLMs) has made AI tangible and accessible to a wider audience, marking a shift from the previously limited to accelerated adoption and making it a class citizen in the business world. This demands more intensive and focused effort to make AI trustworthy.

In today’s landscape, Classical Machine Learning, Deep Learning and Generative AI play pivotal roles in shaping business strategies. Moreover, we’re advancing towards newer realms like Neuro Symbolic Machine Learning and Quantum Machine Learning, sparking the creation of diverse AI roles like AI Engineers, Prompt Engineers, Quantum Engineers and more.

However, there are challenges to navigate alongside the opportunities

  • Regulatory Hurdles: Regulatory frameworks are lagging behind rapid technological advancements, particularly in sectors like healthcare and the public sector, impeding AI adoption. Access and Inclusion: While AI benefits all, the cost of infrastructure and resources needed to harness Large Language Models can exclude smaller players, such as SMEs and academic institutions, as well as disadvantaged regions.
  • Hype vs. Reality: Overshadowed by the hype, Generative AI can appear as a one-size-fits-all solution when, in reality, both Classical AI and Generative AI have their distinct roles and should often work together for optimal outcomes.
  • AI Ethics and accountability: This is one of the biggest concern, especially regarding deep fakes, and generative AI solutionl, How do we evaluate what is real? Is the model free of bias, profanity, and hallucinations?
  • Navigating these challenges with a focus on responsible AI adoption will be pivotal in realizing the full potential of AI and ensuring its benefits are shared globally.

As a senior leader, what strategies do you employ to promote diversity, equity and inclusion within your team and organization?

Keeping your bias away or being aware of them and challenging your own biases, being open to others and valuing what they bring, not what they look like, or not where they come from, what they wear, etc., the fact that everyone deserves the chance, etc. and regularly learning new areas which allow us to encourage diversity, inclusion and equity.

To be more concrete, here are some of the steps that we take

  • Giving everyone a voice.
  • It must be a business strategy, not an HR initiative.
  • Thinking beyond culture fit, as the culture is formed by people; as your team gets more diverse, your culture strengthens and strengthens.
  • Committing to diversity, equity and inclusion in all the interactions
  • Encouraging value-based behaviors.
  • Lead by example
  • Educating the team and leadership

Also, from a data science perspective, you must have different skills and different kinds of people in a team to build AI models that work for everyone. Finally, to ensure trustworthy AI, all these factors are critical; otherwise, you risk producing AI, which is not fair and questionable to adopt.