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, WA, Seattle
The Artificial General Intelligent team (AGI) seeks a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP) and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. As part of this team, you will collaborate with talented peers to create scalable solutions for an innovative conversational assistant, aiming to revolutionize user experiences for millions of Alexa customers. The ideal candidate possesses a solid understanding of machine learning fundamentals and a passion for pushing boundaries in the field. They thrive in fast-paced environments, possess the drive to tackle complex challenges, and excel at swiftly delivering impactful solutions while iterating based on user feedback. Join us in our mission to redefine industry standards and provide unparalleled experiences for our customers. Key job responsibilities . You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. . You will work on core LLM technologies, including developing best-in-class modeling, prompt optimization algorithms to enable Conversation AI use cases · Build and measure novel online & offline metrics for personal digital assistants and customer scenarios, on diverse devices and endpoints · Create, innovate and deliver deep learning, policy-based learning, and/or machine learning based algorithms to deliver customer-impacting results · Perform model/data analysis and monitor metrics through online A/B testing · Research and implement novel machine learning and deep learning algorithms and models. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA | Seattle, WA, USA
US, WA, Redmond
Project Kuiper is an initiative to increase global broadband access through a constellation of 3,236 satellites in low Earth orbit (LEO). Its mission is to bring fast, affordable broadband to unserved and underserved communities around the world. Project Kuiper will help close the digital divide by delivering fast, affordable broadband to a wide range of customers, including consumers, businesses, government agencies, and other organizations operating in places without reliable connectivity. As an Applied Scientist on the team you will responsible for building out and maintaining the algorithms and software services behind one of the world’s largest satellite constellations. You will be responsible for developing algorithms and applications that provide mission critical information derived from past and predicted satellite orbits to other systems and organizations rapidly, reliably, and at scale. You will be focused on contributing to the design and analysis of software systems responsible across a broad range of areas required for automated management of the Kuiper constellation. You will apply knowledge of mathematical modeling, optimization algorithms, astrodynamics, state estimation, space systems, and software engineering across a wide variety of problems to enable space operations at an unprecedented scale. You will develop features for systems to interface with internal and external teams, predict and plan communication opportunities, manage satellite orbits determination and prediction systems, develop analysis and infrastructure to monitor and support systems performance. Your work will interface with various subsystems within Project Kuiper and Amazon, as well as with external organizations, to enable engineers to safely and efficiently manage the satellite constellation. The ideal candidate will be detail oriented, strong organizational skills, able to work independently, juggle multiple tasks at once, and maintain professionalism under pressure. You should have proven knowledge of mathematical modeling and optimization along with strong software engineering skills. You should be able to independently understand customer requirements, and use data-driven approaches to identify possible solutions, select the best approach, and deliver high-quality applications. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. About the team The Constellation Management & Space Safety team maintains and builds the software services responsible for maintaining situational awareness of Kuiper satellites through their entire lifecycle in space. We coordinate with internal and external organizations to maintain the nominal operational state of the constellation. We build automated systems that use satellite telemetry and other relevant data to predict future orbits, plan maneuvers to avoid high risk close approaches with other objects in space, keep satellites in the desired locations, and exchange data with external organizations. We provide visibility information that is used to predict and establish communication channels for Kuiper satellites. We are open to hiring candidates to work out of one of the following locations: Redmond, WA, USA
US, WA, Seattle
Join us in the evolution of Amazon’s Seller business! The Selling Partner Recruitment and Success organization is the growth and development engine for our Store. Partnering with business, product, and engineering, we catalyze SP growth with comprehensive and accurate data, unique insights, and actionable recommendations and collaborate with WW SP facing teams to drive adoption and create feedback loops. We strongly believe that any motivated SP should be able to grow their businesses and reach their full potential by using our scaled, automated, and self-service tools. We aim to accelerate the growth of Sellers by providing tools and insights that enable them to make better and faster decisions at each step of selection management. To accomplish this, we offer intelligent insights that are both detailed and actionable, allowing Sellers to introduce new products and engage with customers effectively. We leverage extensive structured and unstructured data to generate science-based insights about their business. Furthermore, we provide personalized recommendations tailored to individual Sellers' business objectives in a user-friendly format. These insights and recommendations are integrated into our products, including Amazon Brand Analytics (ABA), Product Opportunity Explorer (OX), and Manage Your Growth (MYG). We are looking for a talented and passionate Sr. Research Scientist to lead our research endeavors and develop world-class statistical and machine learning models. The successful candidate will work closely with Product Managers (PM), User Experience (UX) designers, engineering teams, and Seller Growth Consulting teams to provide actionable insights that drive improvements in Seller businesses. Key job responsibilities You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions. About the team The Seller Growth science team aims to provide data and science solutions to drive Seller growth and create better Seller experiences. We structure our science domain with three key themes and two horizontal components. We discover the opportunity space by identifying opportunities with unrealized potential, then generate actionable analytics to identify high value actions (HVAs) that unlock the opportunity space, and finally, empower Sellers with personalized Growth Plans and differentiated treatment that help them realize their potential. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
GB, London
Amazon Advertising is looking for a Senior Applied Scientist to join its brand new initiative that powers Amazon’s contextual advertising product. Advertising at Amazon is a fast-growing multi-billion dollar business that spans across desktop, mobile and connected devices; encompasses ads on Amazon and a vast network of hundreds of thousands of third party publishers; and extends across US, EU and an increasing number of international geographies. We are looking for a dynamic, innovative and accomplished Senior Applied Scientist to work on machine learning and data science initiatives for contextual data processing and classification that power our contextual advertising solutions. Are you excited by the prospect of analyzing terabytes of data and leveraging state-of-the-art data science and machine learning techniques to solve real world problems? Do you like to own business problems/metrics of high ambiguity where yo get to define the path forward for success of a new initiative? As an applied scientist, you will invent ML and Artificial General Intelligence based solutions to power our contextual classification technology. As this is a new initiative, you will get an opportunity to act as a thought leader, work backwards from the customer needs, dive deep into data to understand the issues, conceptualize and build algorithms and collaborate with multiple cross-functional teams. Key job responsibilities * Design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both analysis and business judgment. * Collaborate with software engineering teams to integrate successful experiments into large-scale, highly complex Amazon production systems. * Promote the culture of experimentation and applied science at Amazon. * Demonstrated ability to meet deadlines while managing multiple projects. * Excellent communication and presentation skills working with multiple peer groups and different levels of management * Influence and continuously improve a sustainable team culture that exemplifies Amazon’s leadership principles. About the team The Supply Quality organization has the charter to solve optimization problems for ad-programs in Amazon and ensure high-quality ad-impressions. We develop advanced algorithms and infrastructure systems to optimize performance for our advertisers and publishers. We are focused on solving a wide variety of problems in computational advertising like Contextual data processing and classification, traffic quality prediction (robot and fraud detection), Security forensics and research, Viewability prediction, Brand Safety and experimentation. Our team includes experts in the areas of distributed computing, machine learning, statistics, optimization, text mining, information theory and big data systems. We are open to hiring candidates to work out of one of the following locations: London, GBR
ES, M, Madrid
At Amazon, we are committed to being the Earth’s most customer-centric company. The International Technology group (InTech) owns the enhancement and delivery of Amazon’s cutting-edge engineering to all the varied customers and cultures of the world. We do this through a combination of partnerships with other Amazon technical teams and our own innovative new projects. You will be joining the Tools and Machine learning (Tamale) team. As part of InTech, Tamale strives to solve complex catalog quality problems using challenging machine learning and data analysis solutions. You will be exposed to cutting edge big data and machine learning technologies, along to all Amazon catalog technology stack, and you'll be part of a key effort to improve our customers experience by tackling and preventing defects in items in Amazon's catalog. We are looking for a passionate, talented, and inventive Scientist with a strong machine learning background to help build industry-leading machine learning solutions. We strongly value your hard work and obsession to solve complex problems on behalf of Amazon customers. Key job responsibilities We look for applied scientists who possess a wide variety of skills. As the successful applicant for this role, you will with work closely with your business partners to identify opportunities for innovation. You will apply machine learning solutions to automate manual processes, to scale existing systems and to improve catalog data quality, to name just a few. You will work with business leaders, scientists, and product managers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed services. You will be part of team of 5 scientists and 13 engineers working on solving data quality issues at scale. You will be able to influence the scientific roadmap of the team, setting the standards for scientific excellence. You will be working with state-of-the-art models, including image to text, LLMs and GenAI. Your work will improve the experience of millions of daily customers using Amazon in Europe and in other regions. You will have the chance to have great customer impact and continue growing in one of the most innovative companies in the world. You will learn a huge amount - and have a lot of fun - in the process! This position will be based in Madrid, Spain We are open to hiring candidates to work out of one of the following locations: Madrid, M, ESP
IN, KA, Bangalore
Appstore Quality tech team builds tools, using AI and engineering techniques to provide the best quality apps to Amazon Appstore users. We are a team of highly-motivated, engaged, and responsive professionals who enable the core testing and quality infrastructure of Amazon Appstore. Come join our team and be a part of history as we deliver results for our customers. Appstore Quality team's mission is to automate all types of functional, non functional, and compliance checks on apps submitted by appstore app developers to enable north star vision of publishing apps in under 5 hours. Our team uses various ML/AI/Generative AI techniques to automatically detect violations in images and text metadata submitted by developers. We are working on ambitious project AI projects such as building LLM, auto navigate a mobile app to detect inside app issues and violations. We are seeking an innovative and technically strong data scientist with a background in optimization, machine learning, and statistical modeling/analysis. This role requires a team member to have strong quantitative modeling skills and the ability to apply optimization/statistical/machine learning methods to complex decision-making problems, with data coming from various data sources. The candidate should have strong communication skills, be able to work closely with stakeholders and translate data-driven findings into actionable insights. The successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and ability to work in a fast-paced and ever-changing environment. This role involves working closely with Sr Data Scientist, Principal engineer, and engineering team to build ML and AL based solutions in meeting our north start vision. Key job responsibilities • Implement statistical methods to solve specific business problems utilizing code (Python, Scala, etc.). • Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters. • Collaborate with program management, product management, software developers, data engineering, and business leaders to provide science support, and communicate feedback; develop, test and deploy a wide range of statistical, econometric, and machine learning models. • Build customer-facing reporting tools to provide insights and metrics which track model performance and explain variance. • Communicate verbally and in writing to business customers with various levels of technical knowledge, educating them about our solutions, as well as sharing insights and recommendations. • Earn the trust of your customers by continuing to constantly obsess over their needs and helping them solve their problems by leveraging technology • Excellent prompt engineering skillset with a deep knowledge of LLMs, embeddings, transformer models. • Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers About the team In Appstore, “We entertain, and delight, hundreds of millions of people across devices with a vast selection of relevant apps, games, and services by making it trivially easy for developers to deliver”. Appstore team enables the customer and developer flywheel on devices by enabling developers to seamlessly launch and manage their apps/ in-app content on Amazon. It helps customers discover, buy and engage with these apps on Fire TV, Fire Tablets and mobile devices. The technologies we build on vary from device software, to high scale services, to efficient tools for developers. We are open to hiring candidates to work out of one of the following locations: Bangalore, KA, IND
US, WA, Bellevue
Want to be part of the team whose mission is to expand Alexa to new countries, languages, devices and cultures? The Alexa International team makes it happen. Our customers are very diverse in where they live, the languages they speak to Alexa, the devices they use and the content that matters most. In turn, our problems are diverse and need innovative solutions. We are seeking a Senior Applied Science Manager who will play a key role in the next generation of AI powered Conversational Assistants. Key job responsibilities Lead and manage a team of applied and research scientists responsible for building multilingual experiences Collaborate with cross-functional teams to ensure that Amazon’s AI models are aligned with human preferences. Identify and prioritize research opportunities that have the potential to significantly impact our AI systems. Mentor and guide team members to achieve their career goals and objectives. Communicate research findings and progress to senior leadership and stakeholders. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a highly-skilled Senior Applied Scientist, to lead the development and implementation of cutting-edge algorithms and push the boundaries of efficient inference for Generative Artificial Intelligence (GenAI) models. As a Senior Applied Scientist, you will play a critical role in driving the development of GenAI technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Design and execute experiments to evaluate the performance of different decoding algorithms and models, and iterate quickly to improve results - Develop deep learning models for compression, system optimization, and inference - Collaborate with cross-functional teams of engineers and scientists to identify and solve complex problems in GenAI - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA | New York, NY, USA | Sunnyvale, CA, USA
US, NJ, Newark
Employer: Audible, Inc. Title: Data Scientist II Location: One Washington Park, Newark, NJ, 07102 Duties: Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing RedShift, and S3 / edX storage systems. Build relationships with stakeholders and counterparts, and communicate model outputs, observations, and key performance indicators (KPIs) to the management to develop sustainable and consumable products. Explore and analyze data by inspecting univariate distributions and multivariate interactions, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build production-ready models using statistical modeling, mathematical modeling, econometric modeling, machine learning algorithms, network modeling, social network modeling, natural language processing, or genetic algorithms. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production. Position reports into Newark, NJ office; however, telecommuting from a home office may be allowed. Requirements: Requires a Master’s in Statistics, Computer Science, Data Science, Machine Learning, Applied Math, Operations Research, Economics, or a related field plus two (2) years of experience as a Data Scientist, Data Engineer, or other occupation/position/job title involving research and data analysis. Experience may be gained concurrently and must include one (1) year in each of the following: - Building statistical models and machine learning models using large datasets from multiple resources - Working with Customer, Content, or Product data modeling and extraction - Using database technologies such as SQL or ETL - Applying specialized modelling software including Python, R, SAS, MATLAB, or Stata. Alternatively, will accept a Bachelor's and four (4) years of experience. Multiple positions. Apply online: Job Code: ADBL157. We are open to hiring candidates to work out of one of the following locations: Newark, NJ, USA
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a highly-skilled Senior Applied Scientist, to lead the development and implementation of cutting-edge algorithms and push the boundaries of efficient inference for Generative Artificial Intelligence (GenAI) models. As a Senior Applied Scientist, you will play a critical role in driving the development of GenAI technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Design and execute experiments to evaluate the performance of different decoding algorithms and models, and iterate quickly to improve results - Develop deep learning models for compression, system optimization, and inference - Collaborate with cross-functional teams of engineers and scientists to identify and solve complex problems in GenAI - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA | New York, NY, USA | Sunnyvale, CA, USA