Parmida Beigi, an Amazon senior research scientist, is seen smiling into the camera on a sunny day, she is standing on a terrace with houses and a cityscape visible in the background
Parmida Beigi, an Amazon senior research scientist, uses social media to help others grow into machine learning career paths via her handle @bigdataqueen.

On a mission to demystify artificial intelligence

Parmida Beigi, an Amazon senior research scientist, shares a lifetime worth of experience, and uses her skills to help others grow into machine learning career paths.

Parmida Beigi’s career has touched many facets of machine learning and data science. From her PhD research in computer vision and time series forecasting, to her work in Alexa AI end-to-end systems.

Today Beigi pursues — among other things — speech recognition and natural language processing initiatives to help Amazon’s Alexa customers through her work on the local info team. Beigi has led the work of improving the relevance and ranking of entity search traffic (e.g. queries like “Where is Lions Gate Bridge?”).

As a senior ML practitioner, Beigi says she feels that it’s part of her mission to demystify her field for everyone.

The evidence of her passion for helping others is impressively public. Beigi answers questions and offers practical advice on her popular social media, such as Instagram, LinkedIn, and Twitter feeds. Using simple graphics, quick clips, and the handle @bigdataqueen, Beigi invites followers in to her daily life and expertise as a machine learning scientist at Amazon.

To date, generous knowledge-sharing has garnered her close to 100,000 followers. But her journey to ML scientist/social media maven wasn’t always an obvious one.

At first, she thought she might follow in her family’s footsteps and become a medical doctor. Today, she works on ML/AI projects related to spoken language understanding and information retrieval and ranking. So Beigi relates to people who are figuring out their professional path — not all that long ago, she was doing the same thing.

Discovering data science

In high school, Beigi first focused on science as a stepping stone for pre-med programs. But something was missing. Her natural interests gravitated more toward math and computing, and she began to move in that direction, earning a bachelor’s in electrical and computer engineering.

In a LinkedIn post from earlier this year, Beigi wrote about the excitement surrounding generative AI and the need to "demystify generative AI and move beyond the hype".

In college, Beigi took classes that piqued her interest in digital signal processing. While earning her master’s in electrical and computer engineering, she focused her research on compressive sensing, signal processing and image/video processing, which allowed her to hone her skills in a practical setting.

But Beigi hadn’t quite skipped the healthcare field after all. One of her internships involved a research collaboration between the University of British Columbia (UBC) and the BC Cancer Research Center. In that case, the signal processing involved measuring specific biomarkers in patients’ breath with chemo-resistive sensors, and then using statistical methods to seek specific links between lung cancer and smoking habits.

“Back in those days, I didn’t know I was already building foundations for my career in machine learning,” says Beigi.

“The part of signal processing that really interested me was extracting the information embedded in the signals through time-frequency and spatio-temporal representations,” she says, adding that she loved “being able to solve difficult problems and improve human lives.”

Beigi presented her research at the BCCRC Health Sciences Conference and received the best speaker award. This work also resulted in a journal paper published in IEEE Transactions on Biomedical Engineering.

Piqued by machine learning

She continued on at UBC for her PhD in electrical and computer engineering. Her first few years, she was working on computer vision and AI — and found herself increasingly drawn to machine learning.

Every time ML came up at journal paper submissions that she was reviewing, or conferences she attended such as ICASSP and CVPR, she was fascinated by its vast applications, so she started digging deeper.

“I read lots of papers, took several online courses and listened to podcasts, even though there were not many at that point — whatever tool I could find,” she says. “I set up my research environment so I could develop simple ML-based methods based on the data that I gathered for my thesis, to see how it would work compared to the conventional rule-based techniques that we were using before.”

She was hooked.

Beigi realized she could learn a lot more about practical machine learning by integrating it into her research, where it could be used to solve a real-life problem.

She started working with two hospitals in Vancouver, Canada, where she found out there was a need to create technology for image-guided procedures that would allow doctors to ensure accurate placement of epidural needles — which is notoriously tricky.

Analyzing a stream of ultrasound images taken from a patient’s back while the anesthesiologist was inserting a needle, Beigi developed a tool using image processing techniques along with time-series analytics and ML to visualize and localize that needle for the doctor.

Beigi published and presented her research at several peer-reviewed journals and conferences, including her ML-based tracking work which was published in the International Journal of Computer Assisted Radiology and Surgery. She was also the recipient of an NSERC Alexander Graham Bell scholarship, awarded to top-tier Canadian PhD scholars.

After defending her thesis, she took a job as an ML scientist at Boeing where she was leveraging her expertise with sensor processing to work on predictive and prescriptive maintenance of Boeing aircraft.

Starting out on social

That’s also when she began her social media journey.

“I started sharing my learnings as a self-taught ML scientist to give back to the community and to empower aspiring tech talents” Beigi says. “I wasn’t really taught ML at school. During my early grad studies, when I was done with my research and teaching duties, I was studying machine learning on my own.”

It’s this personal, DIY experience that makes her content so relatable.

Which university degrees are best for a career in AI or data science? Is self-supervised learning really just a fancy name for unsupervised learning? How do you get started coding machine learning in Python?

Beigi says the most common questions she answers are about how to get into data science and ML, and people asking if they can still get into the field without a PhD.

“Data science is not limited to tech, all industries have started to benefit from data science and AI solutions,” she answers. “Typically, for a DS/ML generalist, you don’t need a PhD or necessarily a degree in computer science or data science, these may only help you get shortlisted, but what matters most is whether you can get the job done.”

Career advice
Belinda Zeng, head of applied science and engineering at Amazon Search Science and AI, shares her perspective.

She also helps by providing specifics, drilling down into what skills are needed for the job, regardless of degree.

As she expanded her social presence, she also began to consider a move. She knew her next step would be into the U.S. — specifically to Silicon Valley.

She decided to “do more research, gain more targeted knowledge and hands-on practical experience through side projects, and then apply for jobs that were not necessarily closely related to my PhD work, that would challenge me on both the practical and technical side.”

That’s how she ended up at Amazon.

Working at Amazon

Beigi interviewed at a few companies during her job search, and after pondering competing offers, she decided to join Amazon in 2019. More than three years later, she hasn’t looked back.

“While I stayed with the same team for three years, I’ve had the luxury of applying data science across a variety of verticals, which is a part of my experience at Amazon that I really love,” she says. “It’s always Day 1 at Amazon, and customers are at the center of everything we do. Starting with the customers and working backwards, I get to work with end-to-end Alexa components, starting from speech recognition, to natural language understanding, all the way to the final stage where we optimize relevance to best address customers’ queries through learning to ranking techniques.”

Careers in data science
How Jared Wilber is using his skills as a storyteller and data scientist to help others learn about machine learning.

Since Beigi joined Amazon, she has been a member of Amazon mentoring program, and has been consistently working on improving the bar for scientific publications as an AMLC reviewer.

“A great data scientist is curious, they look at the science behind everything, the how and why things work, and identify patterns, correlations and causations. Similar to a data science project itself, isn’t it?” she says. “Just like science, data science is a broad term. Find the kind of data science that is right for you — you know more than you think you do.”

Related content

US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
IN, HR, Gurugram
We're on a journey to build something new a green field project! Come join our team and build new discovery and shopping products that connect customers with their vehicle of choice. We're looking for a talented Senior Applied Scientist to join our team of product managers, designers, and engineers to design, and build innovative automotive-shopping experiences for our customers. This is a great opportunity for an experienced engineer to design and implement the technology for a new Amazon business. We are looking for a Applied Scientist to design, implement and deliver end-to-end solutions. We are seeking passionate, hands-on, experienced and seasoned Senior Applied Scientist who will be deep in code and algorithms; who are technically strong in building scalable computer vision machine learning systems across item understanding, pose estimation, class imbalanced classifiers, identification and segmentation.. You will drive ideas to products using paradigms such as deep learning, semi supervised learning and dynamic learning. As a Senior Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on complex customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth, scientific vision, project management skills, great communication skills, and a drive to achieve results in a unified team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a bold disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities Architect, design, and implement Machine Learning models for vision systems on robotic platforms Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and contribute to long-term vision and roadmap. Work with stakeholders across , science, and operations teams to iterate on design and implementation. Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment Work directly with customers and partners to test prototypes and incorporate feedback Mentor other engineer team members. A day in the life - 6+ years of building machine learning models for retail application experience - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning - Demonstrated expertise in computer vision and machine learning techniques.
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities As an Applied Scientist in the Content Understanding Team, you will lead the end-to-end research and deployment of video and multi-modal models applied to a variety of downstream applications. More specifically, you will: - Work backwards from customer problems to research and design scientific approaches for solving them - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals About the team Our Prime Video Content Understanding team builds holistic media representations (e.g. descriptions of scenes, semantic embeddings) and apply them to new customer experiences supply chain problems. Our technology spans the entire Prime Video catalogue globally, and we enable instant recaps, skip intro timing, ad placement, search, and content moderation.
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field or relevant science experience (publications/scientific prototypes) in lieu of Masters - Experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment - Papers published in AI/ML venues of repute
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
US, WA, Bellevue
Our mission is to provide consistent, accurate, and relevant delivery information to every single page on every Amazon-owned site. MILLIONS of customers will be impacted by your contributions: The changes we make directly impact the customer experience on every Amazon site. This is a great position for someone who likes to leverage Machine learning technologies to solve the real customer problems, and also wants to see and measure their direct impact on customers. We are a cross-functional team that owns the ENTIRE delivery experience for customers: From the business requirements to the technical systems that allow us to directly affect the on-site experience from a central service, business and technical team members are integrated so everyone is involved through the entire development process. You will have a chance to develop the state-of-art machine learning, including deep learning and reinforcement learning models, to build targeting, recommendation, and optimization services to impact millions of Amazon customers. Do you want to join an innovative team of scientists and engineers who use machine learning and statistical techniques to deliver the best delivery experience on every Amazon-owned site? Are you excited by the prospect of analyzing and modeling terabytes of data on the cloud and create state-of-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the DEX AI team. Key job responsibilities - Research and implement machine learning techniques to create scalable and effective models in Delivery Experience (DEX) systems - Solve business problems and identify business opportunities to provide the best delivery experience on all Amazon-owned sites. - Design and develop highly innovative machine learning and deep learning models for big data. - Build state-of-art ranking and recommendations models and apply to Amazon search engine. - Analyze and understand large amounts of Amazon’s historical business data to detect patterns, to analyze trends and to identify correlations and causalities - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.