Sr. Data Scientist

About Us

At Versa Networks, we're revolutionizing the way businesses connect, secure, and optimize their networks. Our mission is to secure anywhere, anytime access to anything. As a leader in Secure SD-WAN, SSE (Secure Service Edge), SASE (Secure Access Service Edge) and Next-generation Managed Services, we are empowering organizations across the globe to transform their IT infrastructure for the modern cloud era. Our innovative products enable enterprises to deliver a seamless, scalable, and secure digital experience, no matter where their users, devices, or applications are located. Founded by industry veterans and backed by premier venture capital firms, Versa is a market leader driving innovation and growth as it positions itself for a future IPO.

We believe in fostering a culture of innovation, collaboration, and customer success. Our team is comprised of passionate, forward-thinking professionals dedicated to driving the future of networking technology. We encourage creativity, offer opportunities for growth, and provide a dynamic environment where our people can thrive and make an impact.

At Versa Networks, we don't just build products - we build relationships, elevate businesses, and shape the digital future. Join us and be part of a fast-paced, cutting-edge company that's making a real difference in how the world connects and communicates.

Job Summary

We are seeking a talented and motivated Data Scientist to join our Machine Learning team. In this role, you will contribute to a wide range of data-driven projects, from exploratory data analysis to building and deploying machine learning models for a variety of Networking, Security and Language use-cases. You will work closely with cross-functional teams to uncover insights, solve complex problems, and drive data-informed decision-making.

Responsibilities

• Data Analysis and Visualization: Conduct exploratory data analysis (EDA) to understand data characteristics and identify patterns. Utilize data visualization techniques to communicate insights effectively.
• Data Preparation and Engineering: Ingest, clean, preprocess, and transform data to prepare it for analysis and modeling. Handle missing values, outliers, and inconsistencies. Labeling datasets appropriately for classification problems.
• Machine Learning: Build, train, and evaluate machine learning models, including traditional algorithms like CNNs, XGBoost, Transformers, Various Graph ML algorithms, Time Series modelling, LLM etc.
• Model Deployment: Collaborate with engineering teams to deploy models into production, ensuring scalability and reliability. You need to be aware of and have experience in model quantization, model evaluation and various deployment formats like ONNX, GGUF etc
• Problem-Solving: Apply critical thinking and problem-solving skills to tackle complex data challenges.
• Collaboration: Work effectively with data engineers, analysts, and domain experts to understand business requirements and translate them into actionable data insights.