Anton Abrarov, Head of AI & Data Science, Norilsk Nickel

Anton Abrarov brings over a decade of experience in programming and applying machine learning solutions across various sectors, including manufacturing. His expertise has significantly impacted business outcomes, contributing over £80M annually to EBITDA. Currently leading the AI & Data Science team at Norilsk Nickel, he successfully established and managed a global team of over 40 professionals, driving innovative AI initiatives and strategies. His career also encompasses academic contributions, including lecturing at universities. As a speaker, he brings knowledge of AI development from scratch, team leadership, and practical applications of machine learning and data science in industry.

Recently, in an exclusive interview with Digital First Magazine, Anton shared his professional trajectory, insights on some of the key trends and challenges anticipated in the data analytics landscape in 2024, significant career milestones, the secret mantra behind his success, future plans, words of wisdom, and much more. The following excerpts are taken from the interview.

Hi Anton. Can you walk us through your journey into technology and what drew you into the AI and data science space?

My journey into technology began over a decade ago in public healthcare, where I was struck by data’s potential to improve health outcomes significantly. Although AI and data science were less mainstream then, I saw an opportunity to make a real difference. This realisation spurred my deeper dive into AI, leading me to explore its applications in various sectors, from healthcare to mining, always with the goal of using data to create tangible benefits.

What do you love the most about your current role?

What I love most about my current role is the exhilarating blend of innovation and impact. Every day, I have the privilege of pioneering new applications of machine learning that not only enhance our technological processes but also significantly boost our operational efficiency and profitability. For example, implementing AI-driven systems like automated process control has reshaped how we manage our vast industrial operations, resulting in substantial financial gains and more sustainable practices.

This role allows me to see the direct results of our initiatives, transforming theoretical possibilities into tangible benefits. Managing parts of our technology process with AI not only propels our company forward but also sets new industry standards, making a real difference in the metals and mining sector. It’s gratifying to lead a team at the forefront of technological innovation, driving change that echoes beyond our company into the entire industry.

How do you help team members improve in some areas? How do you encourage them to be the best of themselves?

One of the most fulfilling aspects of my role is helping each team member realise their full potential. I start by understanding each individual’s unique values and motivations, as well as their current skills and desired career trajectory over the next two to three years. This deep understanding allows me to tailor a development plan that resonates with their personal and professional goals.

Here’s how I put this into action:

  • Personalised Development Plans: Each team member receives a customised development plan that aligns with their career aspirations and the company’s goals. We review these plans quarterly to adapt to new learning opportunities or shifts in company objectives.
  • Regular Check-ins: Frequent discussions ensure that these plans remain relevant and provide an opportunity for feedback and recalibration. This also helps maintain a strong connection between team members’ work and the broader company mission.
  • Encouragement and Support: By creating an environment that values continuous learning and open communication, I encourage team members to experiment, take on new challenges, and learn from failures without fear.

What are some of the key trends and challenges anticipated in the data analytics landscape in 2024?

In 2024, I anticipate several pivotal trends and challenges in the data analytics landscape:

  • MLOps and ML Productisation: As AI and machine learning become integral to business operations, the focus is shifting towards MLOps to streamline and scale AI deployment. The challenge is to develop robust ML systems that can easily transition from development to production, enhancing reliability and performance.
  • Cost-Effective Cloud Solutions: There’s a growing need to balance the scalability of cloud computing with cost management. Many companies are moving towards a hybrid cloud approach. This model combines the flexibility of the cloud with the control and security of on-premise solutions, presenting challenges in integration and management but offering significant cost savings and operational flexibility.
  • Prototyping with LLMs: Large Language Models (LLMs) like OpenAI’s GPT and others are becoming essential tools for innovation. The challenge here is not just technical but also ethical and regulatory, as we must ensure these powerful tools are used responsibly. In 2024, we will see more organisations prototyping with LLMs, mainly using open-source models to drive innovation while managing AI ethics and compliance risks.

Staying ahead of these trends is crucial for driving the company’s strategy and ensuring we harness AI’s full potential responsibly and effectively.

Are there other skills that you think are crucial to the leadership role in this very researchy data science world?

In the fast-evolving field of data science, while technical acumen is vital, soft skills are equally crucial for effective leadership. These include:

  • Communication and Summarisation: Distilling complex technical details into clear, actionable insights is critical. This helps align the team with business objectives and demonstrates our work’s value to non-technical stakeholders.
  • Customised Leadership: Understanding each team member’s individual aspirations and strengths is key. I focus on customising development plans and communication styles according to each member’s needs and career goals, which enhances motivation and productivity.
  • Networking: Building and maintaining robust professional networks helps one stay abreast of industry trends, recruit top talent, and foster partnerships that can lead to new opportunities. Networking also serves as a vital channel for sharing knowledge and promoting a culture of continuous learning within the team.
  • Business Acumen: A leader in the data science realm must not only be proficient in technical skills but also deeply understand business metrics and dynamics. This dual focus ensures that projects are not just innovative but also directly contribute to the organisation’s strategic goals.

What has been the most fulfilling part of your career?

Every phase has been uniquely fulfilling throughout my career, but the most rewarding aspect has been identifying and capitalising on growth opportunities in emerging technologies and industries. Early in my career, I dedicated myself to understanding where significant growth would occur and strategically positioning my skills and efforts to make the most impact.

This foresight led me to focus on AI and data science before these fields became mainstream, particularly in sectors like healthcare and mining, where the potential for technology-driven transformation was immense. Building AI units from scratch and steering these through various stages of maturity has been particularly satisfying. Seeing tangible outcomes, such as the implementation of AI in mining operations, which significantly improved efficiency and safety, or in public health, where our models predicted healthcare demands, has been immensely gratifying.

Moreover, the opportunity to lead and nurture talent and watch team members grow and excel has added a profound sense of accomplishment to my professional journey. It’s not just about the technologies we develop; it’s also about the people and the processes we improve and the knowledge that our work significantly impacts industries and lives.

What are your valuable learnings and un-learnings during your journey to a visionary leader?

I see several valuable learnings now:

  • Forward Planning: Being clear about where I want to be in the next 2-5 years has been crucial. I’ve learned the importance of regularly revising my goals—every quarter or half-year—to stay aligned with the ever-evolving tech landscape. This helps not just in personal growth but in strategically guiding my team towards future readiness.
  • Adaptability: A key learning has been the importance of flexibility. The tech industry’s rapid evolution requires adapting swiftly, whether by pivoting project directions in response to new information or entering completely different sectors to harness emerging opportunities. This adaptability has been essential for both personal development and organisational agility.
  • Broad Knowledge Base: Expanding my expertise across as many areas as possible has also been critical. It’s not just about depth in one’s field but about understanding adjacent technologies, market dynamics, and even global economic factors that influence the sectors we operate in. This broader perspective fosters better decision-making and innovation.
  • Unlearning Traditional Boundaries: Early in my career, I had to unlearn the notion that leaders must strictly adhere to established paths or methods. In the dynamic field of AI, effective leadership often involves challenging the status quo and embracing unconventional approaches to problem-solving and team management.

You have been a recipient of several prestigious awards and accolades over the years including being named as one of the Data Science Leaders to Follow in 2023 among others. Our readers would love to know the secret mantra behind your success.

I have a few principles that have guided me:

  • Follow Your Passion: Success is a byproduct of one’s commitment to areas they are passionate about. My journey in data science and AI was driven by a deep fascination with how data can transform industries and improve lives. By focusing on what excites me, staying motivated even through challenges becomes natural.
  • Set Clear Goals: Visualising where I want to be in the next few years has been crucial. I regularly set and revise strategic goals, not just for myself but also for my teams, ensuring we are always aligned with the evolving technological landscape and market needs.
  • Celebrate and Communicate Success: Sharing achievements with your team and broader network serves multiple purposes. It not only boosts morale but also reinforces the impact of our work. Celebrating these wins fosters a culture of gratitude and recognition, which is vital for sustained motivation and team cohesion.
  • Adaptability: The willingness to pivot and adapt to new industries and technologies has been fundamental. This flexibility has allowed me to stay ahead in the rapidly evolving field of AI.
  • Continuous Learning: Expanding knowledge is not just about depth but breadth. Understanding various subjects, including emerging tech, business strategies, and global trends, has enriched my approach and decision-making capabilities.

What is that one thing which motivates you to become better and better every day? 

My primary motivation is the awareness that our time is finite. This realisation drives me to maximise daily and continuously innovate and improve.

Where do you see yourself in the next 5 years?

In the next five years, I will be at the forefront of creating innovative solutions, though the specific industry may yet be determined. I aim to continue identifying and seizing opportunities where advanced data analytics and AI can make substantial impacts. I plan to keep expanding my knowledge and skills to stay ahead of technological advancements, ensuring that my contributions remain relevant and transformative wherever I am.

What are three tips that you would give data scientists, junior or regular, on how to progress in their careers and have a better experience doing it?

Here are three tips I would recommend to any data scientist, whether just starting or looking to deepen their expertise:

  • Engage in Practical Application Early: Start applying your skills to real-world problems as soon as possible. Participating in hackathons or working on pet projects can provide an invaluable experience beyond theoretical knowledge. This hands-on approach helps you understand the practical challenges and nuances of applying data science and builds a portfolio that can open new opportunities.
  • Commit to Lifelong Learning: The field of data science is continually evolving, with new tools, techniques, and theories developing all the time. Make it a habit to learn something new regularly—whether it’s a new programming language, a new statistical technique, or insights from a recently published research paper. Continuous learning is crucial to staying competitive and innovative.
  • Find a Mentor: Having a mentor who is at least a few years ahead of where you aspire to be can provide guidance, open doors, and help you navigate the complexities of your career path. A mentor can offer not only technical advice but also career development insights that are invaluable for making strategic decisions about your professional journey.

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