MongoDB is a Crain's Best Place to Work in NYC for the Fifth Year in a Row

Jackie Denner
September 30, 2021
#Culture

We’re thrilled to announce that MongoDB has made Crain’s 2021 Best Places to Work in New York City list. This is the fifth year in a row that we’ve ranked among Crain’s top 100 companies in New York City, coming in at #29 for 2021. Among large companies specifically, MongoDB ranks #14 out of 47.

At MongoDB, we are passionate about our mission of freeing the genius within everyone by making data stunningly easy to work with. This means enabling each individual to pursue their vision, whether they are a developer using our products or an employee. At MongoDB, if you have an idea, you get the trust from leadership and autonomy to run with it while excelling in your role. Every employee can see the direct impact they have on the business and product, as well as the inclusive culture we are building.

To drive the personal growth and business impact of our employees, we have committed to developing an open, supportive, and enriching environment for everyone. From meditation sessions and yoga classes to fertility assistance and a generous parental leave policy — the opportunity to make an impact at MongoDB is real and we want to support all of our employees in that journey.

It’s important for us to embody our company values, especially when it comes to “Embracing the Power of Differences.” One way we promote this is through our affinity groups, which support our larger commitment to an inclusive community. Our affinity groups provide a collaborative space for employees to mentor and connect with one another through a common interest or identity. In collaboration with our affinity groups, MongoDB supported organizations fighting for racial justice and equal opportunity through a fundraising campaign in 2020. MongoDB pledged $250,000, and through combined efforts with employees and outside contributors, we donated over $330,000 to organizations fighting for justice.

While employees have worked from home during COVID-19, we’ve provided telehealth options, mental health support, emergency care leave, company-wide days off, and initiatives to increase social connectivity in a virtual environment. As employees begin to return to our offices, employee health and wellbeing, happiness, and success are of utmost importance to us. We are always striving to make sure that MongoDB is a great place to work for everyone.

Hear from some of our New York City employees

Marissa Jasso, Product Marketing Manager

“As a Latina and Native American in the tech industry, it’s not often I come across a company that makes a consistent effort to ensure all members feel included. To me, that’s a real unicorn company. MongoDB is a deep breath. It’s the relief of knowing that every day, I can bring my whole identity to work.”

Paige Jornlin, Manager, Customer Success

“MongoDB has an incredible culture. Not only do we have an amazing team that makes me excited to come to work each day, but there are countless growth opportunities and our leaders show so much care for their people. It's truly special. MongoDB is also deeply committed to embracing differences. Without having such a diverse team, we wouldn’t be able to innovate, challenge the norm, or think about different ways of doing things as much as we do.”

Blake Deakin, Area VP, Technical Services

“We have the opportunity to solve really big, really interesting problems for our customers. There’s a good chance you’ll work on something, see it in the news, and then say, ‘Hey! I helped make that happen.’ For me, that’s one of the most gratifying things about working here.”

Interested in joining MongoDB? We have several open roles on our teams across the globe and would love for you to transform your career with us!

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Three Tips for Writing Your Customer Success Resume

So you’re interested in joining our Customer Success team. We’re thrilled you’ve identified MongoDB as a potential next step in your career! Before applying to one of our many open positions globally, take some time to read through these top three tips for what our Customer Success recruiters look for when reviewing applications. Less is more I’m a big fan of keeping resumes simple. Fun graphics, fonts, colors, and styles may be eye-catching, but at times they can also be distracting. Resumes that are easy to follow get noticed the most and showcase your expertise the best. If a resume’s format is hard to follow or in a hard-to-read font, a recruiter or hiring manager may miss some key points related to your experience that you wanted to highlight. Consider how you will organize the information you are presenting and which accomplishments you will highlight. This can often be an indicator of how you would present information to a customer! It’s a good idea to stick to a format where company names are highlighted in a larger font with short bullet points summarizing your day-to-day responsibilities and accomplishments underneath. Focus on your most recent role or experience that is the closest fit for the role you are applying to. Identify keywords and details in the job description Resumes that highlight key responsibilities and skills listed in the job description really stand out! We do not expect candidates to have all the skills required for our positions since Customer Success can vary across industries and organizations, but be sure to highlight the relevant skills you have that you can bring to MongoDB. It’s great to also showcase that you have a willingness to learn our technology, are coachable, and have progressed in your career. Try to tell a story with your resume and show the Recruiting team the skills you have gained through your past experiences. If you’ve spent any spare time upskilling, I recommend highlighting it to show your enthusiasm for learning and self-development. If you have been involved in any projects outside your core role that you think may be of interest to the team, you can add those too! Highlight details that showcase your experiences My final tip for applying to a role in Customer Success is to share the right details on your resume. Day-to-day responsibilities are great to list, but also make sure to share important details such as what type of customers you work with (SMB vs. Enterprise, for example), how many customers you manage in your portfolio, the region or industry you support, your current KPIs, and any significant achievements you have made in your current or previous roles. We have several teams within Customer Success at MongoDB focusing on different types of customers and regions, so this will help us to identify quickly which team your experience is more aligned with. These are just a few useful tips from my experience hiring for our Customer Success teams here at MongoDB. Be sure to check out our website to learn more about our Global Customer Success program and view our open roles . We hope to see you in the interview process soon! Interested in pursuing a career at MongoDB? We have several open roles on our teams across the globe and would love for you to transform your career with us!

September 30, 2021

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AI-Powered Call Centers: A New Era of Customer Service

Customer satisfaction is critical for insurance companies. Studies have shown that companies with superior customer experiences consistently outperform their peers. In fact, McKinsey found that life and property/casualty insurers with superior customer experiences saw a significant 20% and 65% increase in Total Shareholder Return , respectively, over five years. A satisfied customer is a loyal customer. They are 80% more likely to renew their policies, directly contributing to sustainable growth. However, one major challenge faced by many insurance companies is the inefficiency of their call centers. Agents often struggle to quickly locate and deliver accurate information to customers, leading to frustration and dissatisfaction. This article explores how Dataworkz and MongoDB can transform call center operations. By converting call recordings into searchable vectors (numerical representations of data points in a multi-dimensional space), businesses can quickly access relevant information and improve customer service. We'll dig into how the integration of Amazon Transcribe, Cohere, and MongoDB Atlas Vector Search—as well as Dataworkz's RAG-as-a-service platform— is achieving this transformation. From call recordings to vectors: A data-driven approach Customer service interactions are goldmines of valuable insights. By analyzing call recordings, we can identify successful resolution strategies and uncover frequently asked questions. In turn, by making this information—which is often buried in audio files— accessible to agents, they can give customers faster and more accurate assistance. However, the vast volume and unstructured nature of these audio files make it challenging to extract actionable information efficiently. To address this challenge, we propose a pipeline that leverages AI and analytics to transform raw audio recordings into vectors as shown in Figure 1: Storage of raw audio files: Past call recordings are stored in their original audio format Processing of the audio files with AI and analytics services (such as Amazon Transcribe Call Analytics ): speech-to-text conversion, summarization of content, and vectorization Storage of vectors and metadata: The generated vectors and associated metadata (e.g., call timestamps, agent information) are stored in an operational data store Figure 1: Customer service call insight extraction and vectorization flow Once the data is stored in vector format within the operational data store, it becomes accessible for real-time applications. This data can be consumed directly through vector search or integrated into a retrieval-augmented generation (RAG) architecture, a technique that combines the capabilities of large language models (LLMs) with external knowledge sources to generate more accurate and informative outputs. Introducing Dataworkz: Simplifying RAG implementation Building RAG pipelines can be cumbersome and time-consuming for developers who must learn yet another stack of technologies. Especially in this initial phase, where companies want to experiment and move fast, it is essential to leverage tools that allow us to abstract complexity and don’t require deep knowledge of each component in order to experiment with and realize the benefits of RAG quickly. Dataworkz offers a powerful and composable RAG-as-a-service platform that streamlines the process of building RAG applications for enterprises. To operationalize RAG effectively, organizations need to master five key capabilities: ETL for LLMs: Dataworkz connects with diverse data sources and formats, transforming the data to make it ready for consumption by generative AI applications. Indexing: The platform breaks down data into smaller chunks and creates embeddings that capture semantics, storing them in a vector database. Retrieval: Dataworkz ensures the retrieval of accurate information in response to user queries, a critical part of the RAG process. Synthesis: The retrieved information is then used to build the context for a foundational model, generating responses grounded in reality. Monitoring: With many moving parts in the RAG system, Dataworkz provides robust monitoring capabilities essential for production use cases. Dataworkz's intuitive point-and-click interface (as seen in Video 1) simplifies RAG implementation, allowing enterprises to quickly operationalize AI applications. The platform offers flexibility and choice in data connectors, embedding models, vector stores, and language models. Additionally, tools like A/B testing ensure the quality and reliability of generated responses. This combination of ease of use, optionality, and quality assurance is a key tenet of Dataworkz's "RAG as a Service" offering. Diving deeper: System architecture and functionalities Now that we’ve looked at the components of the pre-processing pipeline, let’s explore the proposed real-time system architecture in detail. It comprises the following modules and functions (see Figure 2): Amazon Transcribe , which receives the audio coming from the customer’s phone and converts it into text. Cohere ’s embedding model, served through Amazon Bedrock , vectorizes the text coming from Transcribe. MongoDB Atlas Vector Search receives the query vector and returns a document that contains the most semantically similar FAQ in the database. Figure 2: System architecture and modules Here are a couple of FAQs we used for the demo: Q: “Can you explain the different types of coverage available for my home insurance?” A: “Home insurance typically includes coverage for the structure of your home, your personal belongings, liability protection, and additional living expenses in case you need to temporarily relocate. I can provide more detailed information on each type if you'd like.” Q: “What is the process for adding a new driver to my auto insurance policy?" A: “To add a new driver to your auto insurance policy, I'll need some details about the driver, such as their name, date of birth, and driver's license number. We can add them to your policy over the phone, or you can do it through our online portal.” Note that the question is reported just for reference, and it’s not used for retrieval. The actual question is provided by the user through the voice interface and then matched in real-time with the answers in the database using Vector Search. This information is finally presented to the customer service operator in text form (see Fig. 3). The proposed architecture is simple but very powerful, easy to implement, and effective. Moreover, it can serve as a foundation for more advanced use cases that require complex interactions, such as agentic workflows , and iterative and multi-step processes that combine LLMs and hybrid search to complete sophisticated tasks. Figure 3: App interface, displaying what has been asked by the customer (left) and how the information is presented to the customer service operator (right) This solution not only impacts human operator workflows but can also underpin chatbots and voicebots, enabling them to provide more relevant and contextual customer responses. Building a better future for customer service By seamlessly integrating analytical and operational data streams, insurance companies can significantly enhance both operational efficiency and customer satisfaction. Our system empowers businesses to optimize staffing, accelerate inquiry resolution, and deliver superior customer service through data-driven, real-time insights. To embark on your own customer service transformation, explore our GitHub repository and take advantage of the Dataworkz free tier .

November 27, 2024