Abdigani Diriye is seen giving a talk in front of a map of the African continent
Abdigani Diriye was included in a recent ranking of the top 200 young African economic leaders by Institut Choiseul. Born in Somalia, Diriye is passionate about using science to unleash innovation in Africa, and says his research is closely aligned with that goal.
Credit: Bret Hartman / TED

Abdigani Diriye named among top young African economic leaders

The Amazon research manager was included on a list of individuals 40 and younger who are projected to play a leading role in Africa’s economic future.

Over the past decade, the tech industry in Africa has witnessed explosive growth. According to Briter Bridges, the number of tech hubs on the continent has almost doubled over the past few years. Countries on the African continent have the potential for profound economic change — change that will be driven by technology.

Abdigani Diriye has spent several years contributing to that transformation as part of a career spent thinking about how technology can help accelerate economic development. That commitment led to his inclusion in a recent ranking of the top 200 young African economic leaders by Institut Choiseul. The Paris-based think tank, which is dedicated to the analysis of contemporary strategic issues and international economic affairs, recognizes leaders age 40 and younger who are projected to play a leading role in Africa’s economic future.

Born in Somalia, Diriye moved to England in 1989. He is passionate about using science to unleash innovation in Africa and said that his research in academia and at companies like Amazon is closely aligned with that goal.

“I’m humbled to be included among CEOs and other business leaders in the rankings released by Institut Choiseul,” said Diriye. “I’m also excited that they are beginning to include scientists on the list, and recognizing the prominent role science will have to play in accelerating economic development in Africa.”

Today, as a research manager in Alexa’s Text-to-Speech team in the United Kingdom, Diriye helps develop new models that enable Alexa to talk with users more naturally. Neural text-to-speech models used by Diriye’s team enable Alexa to sound more natural, and change speaking style to match different interactions.

In a conversation with Amazon Science, Diriye spoke about his journey in becoming a scientist, his contributions to furthering innovation in Africa, and how science is poised to change the economic landscape in Africa in the near future.

Q. How did you develop an interest in science?

My first significant interaction with technology was quite literally an electrifying one. I was seven years old and was living in Somalia at the time. My uncle had a transistor radio in his home. I was fascinated by the device, and how this small box could bring us voices and music from faraway countries. I took the radio apart to see how it worked. To this day, I vividly remember the electrical shock I received while reassembling the parts.  The shock was thankfully a mild one, and I was able to move on from assembling transistor radios to math and coding.

Q. Why did you decide to persist with science after moving to England?

Throughout my career, my motivation always remained the same — to move beyond developing technology for technology’s sake, but to understand how it can be used to help humans forge connections with each other, and improve our lives.

Abdigani Diriye at TEDGlobal 2017 - Builders, Truth Tellers, Catalysts - August 27-30, 2017, Arusha, Tanzania
“I’m humbled to be included ... in the rankings released by Institut Choiseul. I’m also excited that they are beginning to include scientists on the list."
Credit: Bret Hartman / TED

The early 2000s saw an explosion in the world of big data. This was also a time when we saw a plethora of machine learning tools released, which in turn enabled us to make sense of all this data. I pursued research in data mining and modeling while obtaining my bachelor’s degree (at the Queen Mary University of London), my master’s degree in advanced computing from King’s College London, and a PhD in computer science, focusing on information retrieval and human-computer interaction, at University College London.

I was also incredibly fortunate to have been given the opportunity to pursue people-focused science in the United States. During a postdoc at Carnegie Mellon University’s Human Computer Interaction Institute, I worked with my colleagues to conduct research on how people can build on each other’s learning as they look up information online. This increases the speed, as well as the depth, of their sensemaking — the amount of information a person can glean about a particular topic online – across a variety of fields. I also contributed to research that created classifiers to understand why users abandon searches and designed systems for multi-session, exploratory search systems.

Q. How has some of your work in academia and the industry contributed to economic development in Africa?

I’ve always had a deep emotional connection to Somalia and Africa at large, and have consciously focused on the many ways science can be used to accelerate economic development in Africa.

I remain tremendously energized about the opportunity to shape the lives of people using science and technology in Africa.
Abdigani Diriye

In 2016, I helped develop a system tailored to underbanked individuals that helped determine the creditworthiness of consumers based on mobile phone usage patterns. We’ve white-labeled the platform, and made it available to banks, government institutions and businesses in Africa. Determining the eligibility of a person to receive credit is especially applicable in many emerging economies, where there is a paucity of consumer data. More than 2 billion individuals around the world are unable to access financial services like credit lines, savings accounts, and insurance because they aren’t able to establish a credit score or pass a KYC check (Know-Your-Customer). This prevents millions of people from forging a path out of poverty.

I’m also on the board of directors for Innovate Ventures, a Somali startup accelerator. In this role, I have been fortunate to have worked with so many talented people such as the team behind e-commerce shopping company Muraadso. The platform combines e-commerce services with brick-and-mortar stores, so that customers in a country with limited internet access are first able to see products in person before buying them.

I remain tremendously energized about the opportunity to shape the lives of people using science and technology in Africa.

Q. What are some of the ways you see science accelerating economic development in Africa?

We are at the beginning when it comes to leveraging science and machine learning to better the lives of people on the African continent. I’m particularly excited about three discrete application areas.

The first is related to leveling the playing field when it comes to education. When a child is able to ask a smart assistant a question in her local language, and get an answer, she now has access to the same information as a child in Europe or America.

Looking for innovation in unexpected places
Abdigani Diriye gave a TED talk about innovation in Africa and the inventiveness to be found in countries like his native Somalia.

Fintech also represents another massive area of opportunity in Africa. Over 60% of the adult population in Africa doesn’t have access to formal banking. I’m awed by the innovative ways countries in Africa have tailored science and technology to meet their unique needs and enable financial inclusion. This is not something that’s particularly well known outside Africa, but more than 40 million people in Kenya today — some of them without bank accounts — use a digital currency called M-PESA to buy goods and services via credits stored on their mobile accounts.

Lastly, Africa is such a large continent. The continent is larger than the United States, China, India, Mexico, and many countries in Europe combined. Given the current state of infrastructure in many parts of Africa, I see innovations like drones playing an increasingly important role in delivering goods and services to people. The revolution in transportation and logistics is already well underway. To give just one example, Rwanda is starting to use drone delivery for its transfusion blood supply.  A hospital can place an order for blood and receive delivery within an hour of placing the order.

Amazon is working on many similar problems – be it leveling access to information, developing new ways of paying for goods and services or new ways to ship products to customers. I love working at Amazon for the same reason that I got interested in pursuing a career in science.  At Amazon, I’m able to work on projects that have a tangible benefit on the lives of millions of people. As humans, we all have aspirations no matter where we are, and technology can be shaped to help us meet those aspirations.

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Enterprises want to use AI agents for critical business workloads that touch legacy desktop applications and mainframes, yet 75% of organizations run legacy applications that lack modern APIs, and 90% of corporate data remains locked in systems never designed for agents. Agentic WorkSpaces solves this: it gives enterprises a secure, governed environment where agents and humans operate both legacy and modern applications directly, just as an employee would, without costly migrations.
IN, HR, Gurugram
Work on ML teams building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. As an Applied Scientist II, you will set scientific direction, mentor applied scientists, and partner with engineering and product leaders to deliver production-grade ML solutions at massive scale. Key job responsibilities 1. Lead and grow a high-performing team of Applied Scientists, providing technical guidance, mentorship, and career development. 2. Define and own the scientific vision and roadmap for ML solutions powering large-scale transportation planning and execution. 3. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 4. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 5. Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 6. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing. A day in the life Your day includes reviewing model performance and business metrics, guiding technical design and experimentation, mentoring scientists, and driving roadmap execution. You’ll balance near-term delivery with long-term innovation while ensuring solutions are robust, interpretable, and scalable. Ultimately, your work helps improve delivery reliability, reduce costs, and enhance the customer experience at massive scale.
US, NY, New York
At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research
IN, KA, Bengaluru
Amazon Ads is a multi-billion dollar global business that delivers advertising experiences across Amazon's owned-and-operated properties (including Prime Video, Twitch, Fire TV, and Amazon.com), third-party publisher networks, and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon, we operate at unprecedented scale across desktop, mobile, connected TV, and emerging surfaces. Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods. We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience. Key job responsibilities As a Data Scientist II in Traffic Quality, you will solve inherently hard problems in advertising fraud detection by applying advanced statistical techniques and machine learning. You'll work on systems that process billions of ad impressions and clicks per day, using Amazon's cloud services including EC2, S3, EMR, Sagemaker, and RedShift. - Define and frame new research problems in fraud detection where neither problem nor solution is well-defined. - Apply new machine learning approaches, models, and algorithms to detect sophisticated invalid traffic. - Apply domain knowledge to perform broad data analysis as a precursor to modeling and build business insights. - Work with unstructured and massive datasets to deliver results. - Produce research reports meeting top-tier external publication standards. - Mentor and develop junior scientists on the team. About the team Here are a few papers published by the team: 1/ [Scaling Generative Pre-training for User Ad Activity Sequences. AdKDD 2023.](https://assets.amazon.science/b7/42/03be071743d5a57cb1656e6caa34/scaling-generative-pre-training-for-user-ad-activity-sequences.pdf) 2/ [SLIDR: Real-time Robot Detection On Online Ads, IAAI 2023, Deployed Highly Innovative Applications of AI Track (AAAI 2023)](https://assets.amazon.science/75/2f/3b7106b143f38f7f4d2806388ace/real-time-detection-of-robotic-traffic-in-online-advertising.pdf) 3/ [Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers, NeurIPS 2022, First Table Representation Learning Workshop](https://openreview.net/forum?id=wIIJlmr1Dsk)