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Abnormal Security Interview Questions

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<p>Let’s say we have a table with an <code>id</code> and <code>name</code> fields. The table holds over 100 million rows and we want to sample a random row in the table without throttling the database.</p>

<p>Write a query to randomly sample a row from this table.</p>

<p><strong>Input:</strong></p>

<p><code>big_table</code> table</p>

<table>
<thead>
<tr>
<th>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>


Write a query to randomly sample a row from a big table.

Random SQL Sample

<p>Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.</p>

<p>How would you assess the validity of the result?</p>


Experiment Validity

<p>You are testing hundreds of hypotheses with many <em>t</em>-tests. What considerations should be made?</p>


<p>What is a key disadvantage of the F-test when compared to multiple t-tests?</p>

What is a key disadvantage of the F-test when compared to multiple t-tests?

Hundreds of Hypotheses

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

Abnormal Security Salaries by Position

<h3>Job Description</h3><div>Job Description<div>About The Role<p>Abnormal Security is looking for a Machine Learning Engineer to join the Message Detection - Attack Detection team. At Abnormal, we protect our customers against nefarious adversaries who are constantly evolving their techniques and tactics to outwit and undermine the traditional approaches to Security. That's what makes our novel behavioral-based approach so…<em>Abnormal. </em>Abnormal has constantly been named as one of the top cybersecurity startups and our behavioral AI system has helped us win various cybersecurity accolades resulting in being trusted to protect more than 8% of the Fortune 1000 ( and ever growing ).</p><p>In a landscape where a single successful attack can lead to financial losses of millions of dollars, the Attack Detection team plays the central role of building an extremely high recall Detection Engine that can operate on hundreds of millions of messages at milliseconds latency. The Attack Detection team's mission statement is to provide world-class detector efficacy to tackle changing attack landscape using a combination of generalizable and auto trained models as well as specific detectors for high value attack categories.</p><p>This team is solving a multi-layered detection problem, which involves modeling communication patterns to establish enterprise-wide baselines, incorporating these patterns as robust signals, and combining these signals with contextual information to create extremely precise systems. The team builds discriminative signals at various levels including message level (eg. presence of particular phrases), sender-level (eg.frequency of sender) and recipient level (eg.likelihood of receiving a safe message). These signals are then combined and utilized to train highly accurate model based as well as heuristic detectors. Additionally, to continuously adapt to new unseen attacks, the team builds out different stages in our automated model retraining pipelines including data analytics and generation stages, modeling stages, production evaluation stages as well as automated deployment stages.</p><p>This role would also have an opportunity to have a significant impact on the overall charter, direction and roadmap of the team. The Machine Learning Engineer would be involved in understanding the domain of false negatives i.e. the current and future attacks which can cause significant customer workflow disruption. They would help define the technical roadmap required to address the most pressing customer problems and simultaneously operate our detection decisioning system at an extremely high recall.</p>What You Will Do <ul><li>Design and implement systems that combine rules, models, feature engineering, and business and product inputs into an email detection product, with senior engineer guidance.</li><li>Understand features that distinguish safe emails from email attacks, and how our model stack enables us to catch them. </li><li>Identify and recommend new features groups or ML model approaches that can significantly improve detection efficacy for a product. Work with infrastructure & systems engineers to productionize signals to feed into the detection system.</li><li>Writes code with testability, readability, edge cases, and errors in mind.</li><li>Train models on well-defined datasets to improve model efficacy on specialized attacks</li><li>Actively monitor and improve FN rates and efficacy rates for our message detection product attack categories, through feature engineering, rules and ML modeling.</li><li>Analyze FN and FP datasets to categorize capability gaps and recommend short term feature and rule ideas to improve our detection efficacy.</li><li>Contribute in other areas of the stack: building and debugging data pipelines, or presenting results back to customers in our tools when the occasion arises</li></ul>Must Have<ul><li>3+ years experience designing, building and deploying machine learning applications in one of the domains of text understanding, entity recognition, NLP experience, computer vision, recommendation systems, or search.</li><li>1+ years of experience with writing stable and production level pipelines for model training and evaluation leading to reproducible models and metrics.</li><li>Experience with data analytics and wielding SQL+pandas+spark framework to both build data and metric generation pipelines, and answer critical questions about system efficacy or counterfactual treatments.</li><li>Ability to understand business requirements thoroughly and bias toward designing a simplest yet generalizable ML model / system that can accomplish the goal.</li><li>Uses a systematic approach to debug both data and system issues within ML / heuristics models.</li><li>Fluent with Python and machine learning toolkits like numpy, sklearn, pytorch and tensorflow.</li><li>Effective software engineering skills who can find answers quickly from code base and writes structured, readable, well tested and efficient code.</li><li>BS degree in Computer Science, Applied Sciences, Information Systems or other related engineering field</li></ul>Nice To Have <ul><li>MS degree in Computer Science, Electrical Engineering or other related engineering field</li><li>Experience with big data, statistics and Machine Learning</li><li>Experience with algorithms and optimization</li></ul><p><strong>This position is not: </strong></p><ul><li>A role focused on optimizing existing machine learning models </li><li>A research-oriented role that's two-steps removed from the product or customer</li><li>A statistics/data science meets ML role</li></ul><p></p><p>#LI-RT1</p><div><div><hr /><p><br />At Abnormal Security certain roles are eligible for a bonus, restricted stock units (RSUs), and benefits. Individual compensation packages are based on factors unique to each candidate, including their skills, experience, qualifications and other job-related reasons. We know that benefits are also an important piece of your total compensation package. Learn more about our Compensation and Equity Philosophy on our Benefits & Perks page.</p></div><div>Base salary range:</div><div>$148,800—$175,000 USD</div></div></div></div>

<h3>Job Description</h3><div>Job Description<div>About The Role<p>Abnormal Security is looking for a Software Engineer II who is a solid software developer with a strong interest in Security & Privacy to join the Platform Security team. The Platform Security team owns the Security and Privacy platform services and infrastructure to uphold industry standards for the company's security posture and customer data protection. This role will design, develop, and release Secure Service to Service communication frameworks and toolings, Customer Data Security & Privacy workflows, and then integrate them with Security Operational analytics and auditing services to monitor, protect, and secure Abnormal infrastructure. The ideal candidate will have:</p><ul><li>Security mindset - ability to identify potential security flaws in any step of the software development lifecycle.</li><li>Ability to translate Security & Privacy requirements into technical requirements and drive end to end execution of the projects.</li><li>Opportunity to facilitate and drive cross team collaboration with product engineering teams to adopt security best practices and secure-by-design platform services and components.</li><li>Opportunity to grow into Senior Software Engineer on the team and deliver bigger scope, higher impact, and additional leadership responsibilities.</li></ul>What You Will Do <ul><li>Own, lead, and deliver the Platform Security features, services, and components for Abnormal microservices such as Zero-Trust networking, Secure Service to Service communications, Customer Data Security & Privacy, Secret Management, etc…</li><li>Design and develop secure and scalable customer integration frameworks and toolings to integrate Abnormal ecosystem with external applications such as Salesforce, Slack, Github, Zoom, etc… to enable Secure by design Multi Product Platform.</li><li>Integrate Platform and Infrastructure components with Security Operational analytics and auditing tooling such as Okta, Dazz, Splunk, SIEM.</li><li>Utilize prior industry experience to contribute and deliver the Platform Security team and the Platform & Infrastructure division roadmap.</li><li>Reconcile Platform and Security requirements to build a secure platform while enabling global engineering teams to deliver high quality software at high velocity.</li><li>Contribute to vendor review and selection for related software purchases in key problem areas such as VPN solution, Just In Time access, and other Security monitoring tools.</li></ul>Must Have<ul><li>BS degree in Computer Science, Electrical Engineering, Computer Engineering or other related engineering field.</li><li>2+ years of experience as a Software Engineer.</li><li>Strong Computer Science and programming fundamentals.</li><li>Experience with system design and cloud infrastructure.</li><li>Experience with Golang and Python programming languages.</li><li>Proven track record of leading feature development and delivering high quality software.</li></ul>Nice To Have <ul><li>Knowledge of Authentication & Authorization and Network Security problem space, best practices, and related technologies.</li><li>Understanding of distributed computing, CAP theorem, and system design tradeoffs.</li><li>Ability to differentiate must-have versus nice-to-have Security and Privacy requirements.</li></ul><p>#LI-RT1</p><div><div><hr /><p><br />At Abnormal Security certain roles are eligible for a bonus, restricted stock units (RSUs), and benefits. Individual compensation packages are based on factors unique to each candidate, including their skills, experience, qualifications and other job-related reasons. We know that benefits are also an important piece of your total compensation package. Learn more about our Compensation and Equity Philosophy on our Benefits & Perks page.</p></div><div>Base salary range:</div><div>$148,800—$175,000 USD</div></div></div></div>

<h3>Job Description</h3><div>Job Description<div>About The Role<p>Abnormal Security is looking for a Senior Machine Learning Engineer to join the Message Detection Decisioning team. At Abnormal, we protect our customers against nefarious adversaries who are constantly evolving their techniques and tactics to outwit and undermine the traditional approaches to Security. That's what makes our novel behavioral-based approach so…<em>Abnormal. </em>Abnormal has constantly been named as one of the top cybersecurity startups and our behavioral AI system has helped us win various cybersecurity accolades resulting in being trusted to protect more than 8% of the Fortune 1000 ( and ever growing ).</p><p>In a landscape where a single successful attack can lead to financial losses of millions of dollars, the Message Detection Decisioning team plays the central role of building an extremely precise Detection Engine that can operate on hundreds of millions of messages at milliseconds latency. Every email ingested by Abnormal flows through the workflow owned by the Detection Decisioning team which applies hundreds of signals and detectors on a message based on the message and user context. The system then computes the final overall decision for the system and consequently chronicles attribution to drive various offline and online metrics such as offline precision, online precision, online False Negative Rate etc.</p><p>This team is solving a multi-layered detection problem, which involves modeling communication patterns to establish enterprise-wide baselines, incorporating these patterns as robust signals, and combining these signals with contextual information to create extremely high precision systems. The team builds discriminative signals at various levels including message level (eg. presence of particular phrases), sender-level (eg.frequency of sender) and recipient level (eg.likelihood of receiving a safe message) which forms the foundation to create highly accurate heuristic and model based detectors. Additionally to maintain an overall high precise detection system, the team innovates on software systems and processes which can be quickly adapted to solve trends seen in the short term as well as generalize well in the longer term.</p><p>This role would also have an opportunity to have a huge impact on the overall charter, direction and growth of the team. The Senior Machine Learning Engineer would be involved in understanding the most pressing customer problems in the domain of false positives and build out the associated technical roadmap to continuously operate our detection decisioning system at an extremely high precision.</p>What You Will Do <ul><li>Design and implement systems that combine rules, models, feature engineering, and business and product inputs into an email detection product.</li><li>Identify and recommend new features groups or ML model approaches that can significantly improve detection efficacy for a product. Work with infrastructure & systems engineers to productionize signals to feed into the detection system.</li><li>Understand features that distinguish safe emails from email attacks, and how our detector stack enables us to catch them. </li><li>Be the expert in main detection pipelines and decision data flow to be able to drive debugging in systematic degradations caused by bad detectors.</li><li>Writes code with testability, readability, edge cases, and errors in mind.</li><li>Train models on well-defined datasets to improve model efficacy on specialized attacks</li><li>Actively monitor and improve False Positive rates and efficacy rates for our message detection product attack categories, through feature engineering, rules and ML modeling.</li><li>Analyze False Negative and False Posi datasets to categorize capability gaps and recommend short term feature and rule ideas to improve our detection efficacy.</li><li>Contribute in other areas of the stack: building and debugging data pipelines, or presenting results back to customers in our tools when the occasion arises</li><li>Lead the team's medium and long term roadmap and drive planning and execution strategy for the pod.</li><li>Coach and mentor junior engineers to uplevel their code quality and ML effectiveness by providing quality code reviews and design reviews.</li><li>Participate in building a world-class detection engine across all layers - data quality, feature engineering, model development, experimentation and operation.</li></ul>Must Have <ul><li>Track record of success in translating business requirements into scalable, maintainable systems with a bias toward simpler but iterative systems.</li><li>4+ Experience with production ML systems - understands the pillars of a modern ML stack and the development, maintenance and tuning processes of ML models.</li><li>Uses a systematic approach to debug both data and system issues within ML / heuristics models.</li><li>Fluent with Python and machine learning libraries like numpy and scikit-learn.</li><li>Experience with data analytics and wielding SQL+pandas+spark framework to both build data and metric generation pipelines, and answer critical questions about system efficacy or counterfactual treatments. </li><li>Independently responsible for the entire lifecycle of projects or features including eng design, development, and deployment.</li><li>Works well with other stakeholders - has worked with cross-functional teams to drive projects over the finish-line.</li><li>Machine learning academic background (Bachelor's degree in Computer Science or related fields).</li></ul>Nice to Have <ul><li>MS degree in Computer Science, Electrical Engineering or other related engineering field</li><li>Experience with big data or statistics</li><li>Familiarity with cyber security industry</li></ul><strong>This Position Is Not</strong><ul><li>A role focused on optimizing existing machine learning models </li><li>A research-oriented role that's two-steps removed from the product or customer</li><li>A statistics/data science meets ML role</li></ul><p>#LI-RT1</p><div><div><hr /><p><br />At Abnormal Security certain roles are eligible for a bonus, restricted stock units (RSUs), and benefits. Individual compensation packages are based on factors unique to each candidate, including their skills, experience, qualifications and other job-related reasons. We know that benefits are also an important piece of your total compensation package. Learn more about our Compensation and Equity Philosophy on our Benefits & Perks page.</p></div><div>Base salary range:</div><div>$176,000—$207,000 USD</div></div></div></div>

Abnormal Security Interview Questions

Abnormal Security Interview Guides

Abnormal Security Interview Questions

Abnormal Security Salaries by Position

Abnormal Security Opening Jobs