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About This Role
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.
Requirements
Qualifications* Masters degree in Computer Science/Engineering.
- 8\+ years of experience in data science or a related field.
- Strong proficiency in Python programming language.
- Experience with data analysis and visualization tools (e.g., Pandas, NumPy, Matplotlib, Seaborn).
- Knowledge of machine learning algorithms and techniques.
- Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch).
- Experience with natural language processing (NLP) and large language models (LLMs) is a plus.
- Strong problem\-solving and analytical skills.
- Excellent communication and collaboration skills.
- Preferred Qualifications:
- + Experience with Go programming language.
+ Acceleration frameworks such as RAPIDS, Spark is preferable.
+ Knowledge of cloud platforms (e.g., AWS, GCP, Azure).
+ Experience with data version control and MLOps practices.
Location: Santa Clara
- Applicants must be authorized to work in the US
The pay range for this position at commencement of employment in California, Washington, or New York City is expected in the range of $225,000 \- $300,000\. A candidate’s specific pay within this range will depend on a variety of factors, including job\-related skills, training, location, experience, relevant education, certifications, and other business and organizational needs.
Benefits
Why Versa?
At Versa Networks, we believe in taking care of our people – both professionally and personally. We offer a comprehensive benefits package designed to support the well\-being, growth, and work\-life balance of our employees. When you join our team, you can expect:
- Competitive Salary \& Incentives: We offer a competitive compensation package with and pre\-IPO equity to reward your hard work and dedication.
- Health \& Wellness: Comprehensive medical, dental, and vision insurance plans to ensure you and your family stay healthy and covered.
- Paid Time Off (PTO): Enjoy a generous PTO policy that includes vacation days, sick leave, and paid holidays to recharge and take care of personal matters.
- Flexible Work Environment: We understand the importance of work\-life balance. Enjoy the flexibility of remote work, and hybrid option to create the work schedule that works best for you.
- Professional Development: We believe in continuous learning. Access to training, certifications, and educational resources to help you grow in your career and stay ahead of industry trends.
- Employee Recognition: We celebrate achievements both big and small, with regular recognition programs and awards that highlight your contributions to our collective success.
- Collaborative Culture: Be part of a dynamic, inclusive, and supportive team where innovation and collaboration are at the heart of everything we do.
- Parental Leave: Generous parental leave policies to support you during life's important moments.
At Versa Networks, our benefits are designed to help you thrive both inside and outside the office. Join us and experience a rewarding, fulfilling career in a supportive environment that values your health, happiness, and success.
Versa Networks is an Equal Opportunity Employer. We are committed to providing equal employment opportunities to all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.
Salary Context
This $225K-$300K range is above the 75th percentile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Versa Networks, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($262K) sits 36% above the category median. Disclosed range: $225K to $300K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Versa Networks AI Hiring
Versa Networks has 1 open AI role right now. They're hiring across Data Scientist. Based in Santa Clara, CA, US. Compensation range: $300K - $300K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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