AfroTech logo and headshots of  Dela Agbemabiese, Justin Barry, Nashlie Sephus and Colby Wise.
With AfroTech World occurring this week, we asked some of the company's Black scientists what they consider some of the systemic issues limiting underrepresented minorities from being more involved in the technology industry. We heard from Dela Agbemabiese (lower left), a data scientist, Justin Barry (upper left), applied scientist, Nashlie Sephus (upper right), applied science manager, and Colby Wise (lower right), senior deep learning scientist.
Credit: Glynis Condon

Issues of racial, ethnic and gender diversity are on the agenda at AfroTech World

Amazon scientists provide insights on issues related to lack of involvement of underrepresented minorities in the technology industry.

As CNBC reported earlier this year, six years after initially disclosing diversity reports, major technology companies have made little progress in hiring more minorities, especially Black employees with science and technology skills.

This presents a series of ongoing challenges. According the US Bureau of Labor Statistics (BLS), nearly one-quarter of the country’s total economic output is produced by high-tech industries, and in 2017 BLS projected there would be more than 1 million job openings in computer and information technology over the next 10 years. Moreover, computing occupation salaries are more than twice the median wage for all other occupations, according to BLS.

“When we look at tech and its impact on our economy, and the simultaneous underrepresentation of the Black community, it is a critically important racial and economic justice issue," says Allison Scott, CEO of the Kapor Center. “When the tech workforce and leadership reflects the diverse experiences and backgrounds of our nation, I believe tech can begin to play an integral role in addressing long-standing disparities that exist in this country.”

As of December 31, 2019, Amazon reported that 26.5% of its global workforce identifies as Black/African American, 26.5% Asian, 18.5% Hispanic/Latinx, 1.3% as Native American, and 3.6% as two or more races.  The 26.5% of employees who identify as Black/African American work in both non-technical and technical roles.

This week at AfroTech World, issues related to the lack of adequate racial, ethnic, and gender diversity within the technology industry are on the agenda as leaders in technology and business come together to exchange ideas for creating greater opportunity for Blacks in technology.  Amazon is a Diamond Sponsor of this year’s event, and has a virtual recruiting booth.  

On Nov. 13, the company is hosting a virtual event, “Our Voices, Our Power”, presented by Amazon’s Black Employee Network (BEN) affinity group. Attendees will hear employees share their Amazon journey stories, learn about career opportunities, and enjoy entertainment.

In advance of AfroTech, Amazon Science asked some of the company’s Black scientists what they consider some of the systemic issues limiting underrepresented minorities’ involvement in the technology industry, about some of the issues they have had to overcome in pursuing their science careers, who or what inspired them to pursue their science careers, and what lessons we might take from their individual experiences. 

Dela Agbemabiese is a data scientist within Amazon’s advertising organization. He earned his master’s degree in business administration from Drexel University.

Dela Agbemabiese
Dela Agbemabiese

What do you consider some of the systemic issues limiting underrepresented minorities from greater employment opportunities in the technology industry?

Lack of financial resources to stimulate curiosity in tech, lack of mentors or heroes to look up to due to low representation, and societal prejudice hindering opportunities.

Lack of financial resources to stimulate curiosity in tech. I have been fortunate and blessed my entire life.  All gratitude goes to my parents. I was born in Ghana, West Africa. My mom was a nurse, and my dad an economist. Due to the nature of my dad’s work, I got the opportunity to travel a lot as a kid, got enrolled into a course at eight years old to get a Linux command line certificate, and always had access to tech resources. My parents sacrificed to ensure I attended the best schools, and there is not a single thing I ever asked for that I did not get. This may not be the case for all children, whose parents are possibly working hard doing multiple jobs, and in some cases are single parents. If the financial resources I had were similar to that of many minority children, it would be unlikely for me to be where I am today.

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Lack of mentors or heroes to look up to due to low representation. While my dad was heavy on econometrics and I learned a thing or two from him, it was my cousin Martey to whom I looked up. He was brilliant academically, and I always wanted to be like him. He tutored me in math and physics, thus giving me an edge over my classmates. Martey was not my only mentor, in fact, I had many, including Yao Obeng, who helped me nurture my creativity and problem-solving skills. Many minority children may not have mentors or heroes within tech to encourage and inspire interest in tech-related careers. If I did not have these mentors to motivate me, it would be unlikely for me to be where I am today.

Societal prejudice hindering opportunities. Growing up in Ghana, prejudice did not exist from a racial standpoint. Once I moved to the United States for my undergraduate degree, this became a reality. My minority friends and I have had to work twice as hard as our peers to prove we are as good as our credentials. We strived to invalidate stereotypes about minorities through the quality of our work and our work ethic. With everything I do, in the back of my mind I am thinking about how my actions or inactions affect the perception towards minorities: am I enabling some of these unfounded prejudices? Or am I, through my work, educating my peers and superiors? For me, this societal prejudice only began when I came to the United States for my undergraduate degree, but imagine the minority children out there who have had to live with this their entire lives. It sure can get demoralizing.

What are some of the obstacles you had to overcome in pursuing your science career?

Societal prejudice hindering opportunities. I have been lucky to have managers and peers that are inclusive and open-minded, that judge me based on the quality of my work. Rachel McKitrick was my first manager in Amazon. I joined Amazon as a business analyst, despite my previous role as a senior data scientist. I just wanted to join Amazon! Rachel knew my business analyst role was not ideal, and gave me projects that were science oriented, which ultimately enabled my transition to scientist. My second manager, Monica Wu, always made herself available to chat and made me feel like my voice and opinion mattered. My current team managed by Dauwe Vercamer and Andrew Petschek welcomed me with open arms, gave me opportunities to shine and lead within the team. They provide direct feedback that has made me a much better scientist today.

I have had the privilege of learning from a lot of people. Societal prejudice may be harder to solve for, but I believe a good place to start will be to find means for minority youth to gain access to some of the brilliant minds within the technology industry, be it through some virtual teaching programs, or through some mentoring programs. The prejudice may exist, the financial resources may be sparse or non-existent, but with heroes and mentors to look up to, a child’s imagination can be sparked for what could be.

Who or what inspired you to pursue your science career, and what lessons can we take from your experience?

My dad due to his econometrics background, and my childhood mentors who encouraged me to put math and science ahead of basketball and soccer. Since then, I have had lots of mentors along the way, especially here at Amazon. Individuals such as Leo Razoumov, Pranjal Mallick, Amy Ruschak, John Lafayette, and Oded Netzer, who have helped shape me into a better scientist.

My advice to Black students interested in a STEM career, or other Black scientists is to find mentors, and get them involved in your work. Meet with them once a week for even 10 minutes, and let them influence your work.

Justin Barry is an applied scientist with Amazon’s Prime Video organization. He earned his master’s degree in computer science from the University of Central Florida.

Justin Barry
Justin Barry

What do you consider some of the systemic issues limiting underrepresented minorities from greater employment opportunities in the technology industry?

This is a massive topic with a myriad of associated socioeconomic issues. One issue that jumps to the forefront for me is the schools where leading companies within the tech industry recruit from. Traditionally, these companies have limited their recruitment to top universities where Blacks and other underrepresented minorities comprise a small percentage of the student population. This is beginning to change, but I believe technology companies need to more aggressively expand their recruitment efforts, especially among historically Black colleges and universities (HBCUs).

What are some of the obstacles you had to overcome in pursuing your science career?

One issue is imposter syndrome — the idea that you're not good enough and you’re only in your position because you’ve been given special treatment. Although imposter syndrome is something everyone experiences, I think it’s particularly acute for Blacks given the clear underrepresentation within the technology industry. Imposter syndrome can touch all aspects of your job if you’re unaware, or if you don’t have the tools to deal with it. Not everyone has the tools to deal with it, and I suspect not everyone has correctly identified the problem.

Who or what inspired you to pursue your science career, and what lessons can we take from your experience?

Video games sparked my interest in computer science, and more specifically artificial intelligence. My undergraduate degree is in computer science and math, and machine learning and AI provide the opportunity to apply my computer science and math skills to real-world applications.

Nashlie Sephus is an applied science manager within Amazon Web Services Ai. She earned her PhD in electrical and computer engineering from Georgia Tech.

Nashlie Sephus
Nashlie Sephus

What do you consider some of the systemic issues limiting underrepresented minorities from greater employment opportunities in the technology industry?

Imposter syndrome is one issue I find common within underrepresented minority groups. It’s a feeling of being convinced that you don’t belong in the industry, or within advanced roles in the industry, regardless of your accolades and accomplishments. It is as if they are not real or didn’t happen. This is often due to not seeing many others who look like you in similar or higher positions. ‘You can’t be what you can’t see’ is a common thought. Also, there are few mentors or support systems for these groups, and as a black/female/engineer/scientist, you sometimes feel like the minority of the minority, which further isolates you.  

What are some of the obstacles you had to overcome in pursuing your science career?

At times, I have had to fight for myself and members of my teams for equal pay and advancement in my career. I also have needed to develop mechanisms to be heard when it was difficult to convey messages to those around me. I’m usually quiet and reserved, but over the years I’ve learned how to gain respect from peers by being more outspoken even, or especially, when I disagreed. This is one reason why I appreciate Amazon’s leadership principle: Have Backbone; Disagree and Commit. 

Who or what inspired you to pursue your science career, and what lessons can we take from your experience?

I grew up in a house full of women where we often did our own chores, like fixing and repairing things around the house. I was also always going to summer math and science camps in elementary and middle school, especially a summer engineering camp for girls after my eighth grade science teacher recommended I attend. This was when I was first introduced to the various areas of engineering, and fell in love with computer science. Being able to control the hardware with software was fascinating to me. I knew then that’s what I wanted to do. This early exposure to science was key to me figuring out one of my passions, in addition to music and sports.

Colby Wise is a senior deep learning scientist and manager within the AWS Machine Learning Solutions Lab. He earned his master’s degree in computer science from the Columbia University Fu Foundation School of Engineering and Applied Sciences.

Colby Wise
Colby Wise

What do you consider some of the systemic issues limiting underrepresented minorities from greater employment opportunities in the technology industry?

Educational opportunity. Science, technology, engineering, and math (STEM) careers in the technology industry are highly competitive. Over the years, we’ve seen advanced tools and technologies like cloud technology, machine learning, and deep learning, that were once reserved only for large companies or prestigious universities being utilized by students as early as junior high school. While this has created and accelerated educational opportunities for millions of students globally, the reality is that not all have been able to benefit. In the United States, public school funding varies significantly by geography, and where you grow up is a major factor in access to educational resources. Schools with advanced STEM courses and other after-school programs are valuable inroads for STEM students to accelerate their learning opportunities and explore careers in science. What’s more, these opportunities compound positively from lower educational levels to higher educational levels. While not the only factor, these programs are important when understanding the pipeline of underrepresented minorities in highly competitive industries like technology. For example, the US Federal Reserve conducted a study highlighting how educational attainment of parents plays an important role in children’s educational pursuits. Studies like this and others indicate that lower parental educational attainment may present a unique challenge for students. One potential consequence of underrepresentation of minorities in advanced degrees is that employment opportunities often arise from one’s social network, employee referrals, for example. This can be summarized as both an employment funnel problem and a network problem. While not always the case, a more diverse workforce can build connections to underrepresented talent pools. 

Financial equality. In a study from 2020, the US Federal Reserve found large and persistent gaps in net wealth and earnings by race and ethnicity. While education is a significant factor in wage gaps, the St. Louis Federal Reserve found net wealth by race was not as positively correlated with educational attainment for minorities. Educational attainment is extremely important. Many highly technical roles require advanced degrees. Financial equality and opportunity as characterized by job salary prospects, current income and net wealth, and access to educational funding sources like loans are all potential factors impacting lower minority employment. In 2016, the Brookings Institution found the median household net wealth for Black and Hispanic families to be 1/8th  that of white households. When you consider the rising cost of college and advanced degrees, this income and net wealth gap may also play a factor in why employment among underrepresented minorities is lower in highly competitive industries like technology. Specifically, minorities whose households cannot readily pay for advanced degrees choose between the implications of high debt burdens and lower comparative earnings, and often must forsake advanced degrees to enter or stay in the workforce.

Leadership representation. Representation of minorities in leadership positions is relatively low. It is unclear how much educational opportunity and financial equality contribute to this, compared to other issues such as equitable pathways to senior leadership positions. In many companies in which I have worked, you notice a similar triangular pattern of minority leadership where representation at junior levels is more in-line with industry trends, while there is a dearth of representation as you reach more senior positions. No doubt there is work to be done to drive greater employment of underrepresented minorities at all levels. But simply increasing the representation at entry levels does not address other attrition and talent-retention hurdles. Overall, companies need to take a more systematic, data-driven approach to move the needle and find solutions to underrepresentation of minorities in the tech industry. For instance, companies should not be afraid to tackle the complex issues at multiple hierarchies, such as creating innovative solutions to drive educational opportunity while objectively measuring current pathways to employment within the tech industry. Furthermore, companies should ensure financial equality by aligning corporate incentives with fair pay distributions, minority leadership representation, and talent development and retention.  

What are some of the obstacles you had to overcome in pursuing your science career?

Educational opportunity. While everyone’s path is different, unfortunately my story is rather common given its similarity to those of many underrepresented minorities. I faced and overcame obstacles in educational and financial opportunity plus roadblocks to leadership roles. I attribute my luck mainly to the many individuals who provided a helping hand, plus a little bit of hard work sprinkled in. I grew up in a single-parent household in an impoverished, high-crime inner-city area. Despite this, my family valued education highly, and one of my parents had an advanced degree which was extremely rare for the area. Given that, I always ranked in the top 1% in my coursework while very young. That said, district educational attainment rates were low, and advanced coursework or programs for gifted students were nonexistent. However, prior to high school an unfortunate family event led to me moving from one of the poorest areas in the country to one of the best school districts nationally. After discovering how far behind I was in math and science, my family and I worked extremely hard over several years to get me back in line with my expected academic grade level. Now fast-forwarding to college: I, like many other minorities, did not have the means to pay for college, nor easy access to loans. After being selected to a number of great schools, my decision was ultimately driven by the amount of money I received in scholarships and grants. During college I followed the same recipe for success: tons of luck, humility to ask for help, and a bit of hard work to land an internship as a sophomore at a prestigious Wall Street investment bank. There I was surrounded by some of the smartest minds in STEM, with many having achieved advanced degrees from top universities around the world. The vast majority of these individuals did not look like me. Desperately wanting to be accepted and succeed among my peers in industry is what drove me to pursue a career in science, and many years later brought me to AWS.

Who or what inspired you to pursue your science career, and what lessons can we take from your experience?

Family and friends. Ultimately, doing what you love and constantly learning while being curious is the greatest inspiration one needs to pursue a career in science. As discussed above, studies have shown a correlation between parental educational attainment and children’s attainment. Thinking forward a bit, I combined my passion for what I love in science — AI/ML — with a selfish goal of wanting to be a living model for a career in science for my children. My greatest inspiration, however, is my wife. She discovered her passion for science at a very young age with plentiful opportunities to explore that passion, ultimately helping her reach the pinnacles of academia, where she received undergraduate and graduate degrees from two of the top universities in the world. Her passion for science, hard work, and humility continue to inspire me on a daily basis.

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Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. The Amazon Music Search Science team is seeking an innovative and driven Applied Scientist to join our engineering and science hub in Bangalore. You will work alongside a world-class team of machine learning experts to break new ground in understanding user intent, classifying complex audio and musical forms, and creating next-generation interactive search experiences that help users find the exact music, podcasts, and audio content they are in the mood for. In this role, you will own the design, development, and deployment of end-to-end machine learning systems. You will balance execution on core search and discovery priorities—such as improving retrieval accuracy, latency, and relevance for millions of daily queries—while laying the foundational modeling capabilities for broader semantic understanding and advanced conversational search experiences across mobile, web, and voice-forward devices (like Alexa and Echo). Key job responsibilities - Core Search & Execution: Collaborate with scientists, software engineers, and product managers to define, frame, and solve complex business and ranking problems as machine learning, information retrieval, or optimization tasks. - Advanced AI & Modeling: Design, build, train, and evaluate production-grade ML models using classical machine learning, deep learning, Large Language Models (LLMs), and Agentic AI techniques to scale music discovery and intent resolution. - End-to-End Production Ownership: Take algorithms from research ideation to production deployment. Build scalable data pipelines, efficient model-serving systems, and robust offline/online evaluation frameworks. - Experimentation & Iteration: Design and analyze large-scale A/B experiments across millions of customers to measure impact on search relevance, engagement, and customer satisfaction, refining models for continuous improvement. - Forward-Looking Innovation: Research and implement novel statistical and machine learning approaches, exploring multi-modal understanding, rich content semantics, and advanced retrieval mechanisms that extend beyond traditional search boundaries. - Technical Communication: Communicate findings, architectural decisions, and technical roadmaps clearly to both technical peers and executive stakeholders, authoring robust design documents and contributing to team standards. Basic Qualifications - PhD, or Master’s degree and 4+ years of relevant experience in Computer Science, Computer Engineering, Machine Learning, Statistics, or a related quantitative field. - 3+ years of hands-on experience building machine learning models or algorithms for business applications and deploying them into production. - Strong programming skills in Python, Java, C++, or related languages, with a solid foundation in data structures, algorithms, and object-oriented design. - Experience in one or more of the following areas: Information Retrieval, Natural Language Processing (NLP), Recommender Systems, Deep Learning, or Numerical Optimization. - Demonstrated ability to work effectively with cross-functional teams in a fast-paced environment. Preferred Qualifications - Experience with large-scale distributed computing frameworks and big data systems (e.g., Spark, Hadoop, AWS infrastructure). - Experience building search ranking, query understanding, or semantic retrieval systems for high-scale consumer applications. - Familiarity with modern foundation models, LLMs, fine-tuning techniques, and efficient inference optimization for production services. - Track record of peer-reviewed publications or patents at top-tier machine learning/AI conferences (e.g., NeurIPS, KDD, ACL, SIGIR, ICML). - Experience in designing, executing, and evaluating rigorous online A/B experiments.
IN, KA, Bengaluru
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. The Amazon Music Search Science team is looking for an execution-focused Senior Applied Scientist to spearhead core scientific initiatives within our Bangalore hub. In this leadership-by-example role, you will define and execute the applied science roadmap for search and content discovery systems, directly impacting millions of customers worldwide. You will operate at the exciting intersection of large-scale search infrastructure, applied machine learning, and foundation models. You will drive immediate, high-impact business deliverables in music search relevance, personalization, and retrieval performance, while simultaneously architecting the long-term technological vision that expands our search ecosystem toward deeper semantic intelligence, agentic workflows, and cross-domain audio understanding. Key job responsibilities - Strategic Roadmap & Architecture: Define and execute the technical and scientific roadmap for music search systems, making critical architectural decisions that balance short-term feature delivery with long-term scalability, maintainability, and extensibility. - Pioneering Applied Science: Lead the design and implementation of state-of-the-art machine learning solutions leveraging deep learning, LLMs, and agentic workflows to solve complex search, ranking, and intent-matching challenges. - Cross-Functional Leadership: Partner closely with product management, engineering leaders, and peer teams to harmonize technical direction and deliver synchronized customer experiences. - Technical Excellence & Mentorship: Drive engineering and scientific excellence across the team by conducting rigorous design reviews, establishing modeling best practices, setting high bars for artifact delivery, and mentoring junior/mid-level scientists. - Experimentation & Scaling: Establish robust scientific processes for large-scale data analysis, offline model validation, and online A/B experimentation, ensuring high statistical rigor and measurable business impact across millions of active listeners. - Stakeholder Influence & Writing: Author strategic whitepapers, and executive-level documentation. Communicate complex technical options and trade-offs to senior leadership to drive informed decision-making. Basic Qualifications - PhD, or Master’s degree and 6+ years of applied research and industrial machine learning experience in Computer Science, Machine Learning, or a related field. - 3+ years of specialized experience designing, building, and scaling machine learning models for core production business applications (e.g., Search, Recommendation Systems, or Large-Scale Information Retrieval). - Expert programming proficiency in Python, Java, C++, or related languages, combined with deep familiarity with neural network architectures and deep learning frameworks. - Proven track record of owning end-to-end technical deliverables from problem formulation and model architecture to production deployment and performance tuning. - Demonstrated leadership in mentoring technical talent and driving engineering/scientific best practices. Preferred Qualifications - Deep expertise in search retrieval, query understanding, ranking algorithms, and large-scale vector search/embedding systems. - Experience building applied science solutions on top of foundation models, large language models (LLMs), or multi-modal architectures. - Experience with large-scale distributed training and inference optimization on cloud infrastructure (AWS). - A strong publication record or patent portfolio in top-tier peer-reviewed venues (e.g., NeurIPS, SIGIR, KDD, ACL, ICML). - Experience designing and interpreting complex online experimentation frameworks for consumer-facing recommendation or search products.
US, CA, Palo Alto
The Demand Utilization team within Amazon Advertising is responsible for determining which ads to serve when hundreds of millions of shoppers search for products on Amazon. We sit at the intersection of customer intent understanding and advertiser value, solving one of the most complex matching problems in the industry, identifying the right ad, for the right shopper, at the right moment, across one of the world's largest product catalogs. Our systems deliver billions of ad impressions and millions of clicks daily under strict relevance and latency constraints. We are looking for a Principal Applied Scientist to set the technical vision and drive the science strategy for our ad retrieval and ranking systems. This is a high-impact leadership role where you will tackle unsolved problems at the frontier of large-scale information retrieval, natural language understanding, and multi-objective optimization, all operating in real time at Amazon scale. You will work on challenges such as: - Modeling shopper intent from sparse, ambiguous, and multi-modal signals - Designing retrieval architectures that balance relevance, advertiser and shopper experience across billions of candidate ads - Advancing personalization and cold-start strategies for new advertisers and emerging product categories This is a role for a scientist who wants to shape the future of performance advertising through rigorous research applied to real-world systems that directly impact Amazon's customers, sellers, and business. Key job responsibilities Key Responsibilities: - Own the science roadmap for ad retrieval and ranking within Demand Utilization, defining multi-year research priorities aligned with business goals - Lead the design and development of novel machine learning models and algorithms for relevance, intent understanding, and ad selection at scale - Drive end-to-end execution from problem formulation and experimentation through production deployment, measuring impact on shopper and advertiser outcomes - Mentor and elevate a team of applied scientists and research engineers, raising the technical bar and fostering a culture of scientific rigor - Collaborate cross-functionally with product, engineering, and business leaders to translate science capabilities into product strategy - Represent Amazon externally through publications at top-tier venues, patents, and participation in the broader ML/IR research community
US, MA, N.reading
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Design and implement whole body control methods for balance, locomotion, and dexterous manipulation - Utilize state-of-the-art in methods in learned and model-based control - Create robust and safe behaviors for different terrains and tasks - Implement real-time controllers with stability guarantees - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation - Mentor junior engineer and scientists