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DataVisor

DataVisor

www.datavisor.com

3 Jobs

163 Employees

About the Company

DataVisor is the world's leading fraud and risk management platform that enables organizations to respond to fast-evolving fraud attacks and mitigate risks as they happen in real time. Its comprehensive solution suite combines patented machine learning technology with native device intelligence and a powerful decision engine to provide protection for the entire customer lifecycle across industries and use cases. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.

Listed Jobs

Company background Company brand
Company Name
DataVisor
Job Title
AI/Machine Learning Engineering Intern (MS/Ph.D. New Grad)
Job Description
**Job Title** AI/Machine Learning Engineering Intern (MS/Ph.D. New Grad) **Role Summary** Internship focused on building and scaling real‑time AI systems for fraud detection. Collaborate with engineers and data scientists to develop data pipelines, distributed backend services, and ML inference pipelines, with exposure to large language models and agentic workflows. **Expectations** - Recently graduated or currently completing an MS or Ph.D. in Computer Science, Machine Learning, AI, Data Science, or related field. - Strong motivation to learn production‑scale AI engineering. - Ability to tackle complex technical problems and grow through mentorship. **Key Responsibilities** - Build and maintain high‑throughput data pipelines using Spark, Kafka, or Flink. - Process and aggregate real‑time signals (device fingerprints, behavioral data) for shared intelligence. - Design and optimize distributed backend systems for low‑latency, high‑volume decisioning. - Develop AI applications and agentic workflows with LLMs (OpenAI, Anthropic, Google). - Deploy, monitor, and integrate supervised and unsupervised ML models into real‑time scoring APIs. - Implement privacy‑first design practices (tokenization, hashing). - Collaborate cross‑functionally with Data Science, Product, and Engineering teams to test models and ship features. **Required Skills** - Proficient in Python programming. - Familiarity with distributed systems, machine learning, data engineering, or backend development. - Experience or coursework in big data frameworks (Spark, Kafka, Flink) a plus. - Understanding of core ML concepts (supervised and unsupervised learning). **Required Education & Certifications** - MS or Ph.D. in Computer Science, Machine Learning, AI, Data Science, or related field (current or recently completed).
Mountain view, United states
Hybrid
Fresher
22-12-2025
Company background Company brand
Company Name
DataVisor
Job Title
Machine Learning Engineer - Fraud Detection
Job Description
**Job Title:** Machine Learning Engineer – Fraud Detection **Role Summary:** Design, develop, and deploy large‑scale unsupervised machine‑learning models that detect fraudulent activity across financial transactions, mobile acquisition, social networks, and commerce. Work with billions of events, collaborate with data scientists and infrastructure engineers, and continuously improve detection accuracy and system performance. **Expectations:** - 2+ years of professional software development in Java or C++. - Strong grasp of data structures, algorithms, and writing optimized code. - 1+ year experience with web back‑end services or big‑data platforms (Hadoop, MapReduce, Spark). - Exposure to machine‑learning or data‑science workflows (preferred). - Ability to handle high‑volume data pipelines and contribute to model enhancements. - Experience in anti‑fraud modeling within the financial sector is a plus. **Key Responsibilities:** - Build and maintain fraud‑detection pipelines on Hadoop/Spark clusters. - Implement, test, and optimize unsupervised ML algorithms for anomaly detection. - Integrate models into production back‑end services and APIs. - Collaborate with data scientists to translate research prototypes into scalable solutions. - Monitor model performance, conduct root‑cause analyses, and iterate improvements. - Write clean, well‑documented, and version‑controlled code adhering to best practices. **Required Skills:** - Java or C++ programming (expert level). - Proficient in core data structures and algorithm design. - Experience with web backend development or big‑data frameworks (Hadoop, MapReduce, Spark). - Familiarity with machine‑learning concepts and tools (Python/R, scikit‑learn, TensorFlow, etc.). - Strong problem‑solving abilities and comfort with large‑scale data processing. - Linux/Unix environment, Git or other source‑control systems. **Required Education & Certifications:** - Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related quantitative field (required). - Master’s or PhD in a relevant discipline (preferred). - Relevant certifications (e.g., AWS Certified Big Data, Google Cloud Professional Data Engineer) are optional but advantageous.
Mountain view, United states
On site
Junior
15-01-2026
Company background Company brand
Company Name
DataVisor
Job Title
Quality Assurance Manager
Job Description
<think> Okay, let's tackle this. The user wants a concise job profile for a Quality Assurance Manager based on the provided job description. First, I need to extract all the key elements without any fluff. The original job title is "Quality Assurance Manager." I'll keep that as is. The job overview mentions leading and scaling QA functions, which I can condense into a Role Summary. The main goal is to own the end-to-end quality strategy, improve automation, apply AI, and build a metrics-driven QA org. Looking at Key Responsibilities: there are several points like owning end-to-end strategy, establishing QA processes, defect management, automation, AI tools, QA KPIs, team leadership, collaboration, and early QA involvement. Need to condense these into bullet points without company-specific details. Requirements: They want 10+ years QA/testing, 5+ in leadership. Strong QA methodologies, test planning, defect lifecycle. Hands-on automation frameworks, CI/CD. Ownership mindset, experience with AI tools in QA, QA metrics and dashboards, cross-functional communication. Need to exclude benefits, location, company info. Focus on skills, education, certs. Education isn't specified, but maybe a bachelor's or higher is implied? Certifications aren't listed, but perhaps mention if required. But since the job description doesn't specify, maybe leave it blank or mention if needed. Check for keywords relevant for ATS. Use terms like QA Manager, test automation, CI/CD, defect management, AI tools, metrics, KPIs. Ensure it's all in English, concise, under 500 words. No fluff, just factual, structured points. Make sure each section is clear and aligned with the original job description. Also, make sure to use bullet points where needed and keep the language neutral and globally understandable. Double-check if all required sections are there: Job Title, Role Summary, Expectations (maybe equivalent to Responsibilities?), Key Responsibilities, Required Skills, Required Education & Certifications. Wait, the user says "Expactations"—probably a typo for "Expectations." But looking at the original instruction, under the output, it's written as "Expactations." Hmm, maybe they meant Expectations. But in the sample output, maybe it's a mistake. The user's instruction says: Output only: Job title, Role Summary, Expactations, Key Responsibilities, Required Skills, Required Education & Certifications. Maybe they meant "Expectations" but had a typo. So I'll follow as written: Expactations, Key Responsibilities. Wait, the user says: "Expactations" but that's misspelled. Let me check the original instruction again. The user wrote: Output only: Job title, Role Summary, Expactations, Key Responsibilities, Required Skills, Required Education & Certifications. So "Expactations"—that's a typo. Probably they meant "Expectations." But I should follow the user's exact wording, even if it's a mistake. So I'll list "Expactations" as per their instruction. But what is the content for "Expactations"? The user probably meant "Expectations" which might be the same as Role Summary or Key Responsibilities. But looking at the example input, in the job description there's a "Position Overview" which might translate to Role Summary. Then "Key Responsibilities" are the bullet points. The "Requirements" section would be "Required Skills," and "Education & Certifications." Wait, the user instruction says: Role Summary, Expactations, Key Responsibilities, Required Skills, Required Education & Certifications. So maybe "Expactations" is meant to represent the expectations from the candidate, which is similar to the requirements. But in the example, the "Requirements" section lists candidate requirements, so maybe "Expactations" is another category for the same. To avoid confusion, I'll map the Requirements to the "Expactations" section. But the user might have intended "Expactations" as the company's expectations from the role, which could be part of the Role Summary. Wait, looking back: Original user instruction says: Provide a concise summary for job matching. The output sections are: Job title, Role Summary, Expactations, Key Responsibilities, Required Skills, Required Education & Certifications. So the user wants: Role Summary: Overview of the role's purpose. Expactations: What the company expects from the candidate (requirements/qualifications). Key Responsibilities: What the role does. Required Skills: Technical and soft skills. Education and certifications. Therefore, the "Expactations" section is where the candidate's qualifications go. So in the job description, under "Requirements" is the candidate's qualifications, which would map to "Expactations" (typos aside). Therefore, the "Expactations" here are the candidate's experience, skills, etc. So in the example, the "Requirements" section becomes the "Expactations" here. Therefore, structure: Job Title: as is. Role Summary: from the Position Overview, which talks about leading QA, scaling functions, delivering improvements. Expactations: the Requirements section (candidate's experience, etc.) Key Responsibilities: the bullet points under the job description's Key Responsibilities. Required Skills: maybe the same as the skills in Requirements, but perhaps listed separately. Wait, the user says Required Skills and Required Education & Certifications. But the original job description combines experience, skills in the "Requirements" section. However, the user wants Role Summary, Expactations (which is the requirements section), Key Responsibilities, Required Skills (maybe separate skills), and Education & Certs. Wait, perhaps the user's instruction is to split the requirements into two sections: Expactations (possibly requirements about experience and leadership) and Required Skills (technical skills like automation frameworks, AI tools, etc.) But the job description's Requirements section has a mix of both experience and skills. For example, "10+ years QA/testing experience... hands-on experience with test automation... experience using AI tools..." So maybe split that into Expactations (experience, leadership) and Required Skills (technical skills). But I need to follow the user's exact structure. Alternatively, the user might want: Expactations: The hard criteria like years of experience, leadership roles. Required Skills: The technical skills like tools, methodologies. So in the original job description, under Requirements: 10+ years QA/testing, 5+ leadership → Expactations. Strong knowledge of QA methodologies → Required Skills. Hands-on automation frameworks → Required Skills. Experience with AI tools → Required Skills. Communication skills → Required Skills. But the user's instruction says Required Skills and Required Education & Certifications. Maybe the Required Skills would include QA methodologies, test planning, defect lifecycle, test automation frameworks, AI tools, metrics, communication. Education and Certifications: if there are any specified. The original job description doesn't mention education, so maybe leave it as "Bachelor’s degree in Computer Science or related field" as a standard assumption, since it's not specified. But the user says to exclude fluff. Since the job description doesn't mention education, perhaps leave it blank or just note "Bachelor's degree or equivalent" if inferred. Putting it all together: Job Title: Quality Assurance Manager Role Summary: Lead and scale Quality Assurance function, drive end-to-end quality strategy, improve automation, apply AI for efficiency, build metrics-driven QA organization. Expactations: 10+ years QA/testing, 5+ leadership, proven scaling QA processes, execution focus during releases, ownership mindset. Key Responsibilities: [list from the job desc] Required Skills: QA methodologies, test planning, defect lifecycle, test automation, CI/CD integration, AI tools, QA KPIs/metrics, communications Education & Certifications: Bachelor's or higher in CS or related (assuming, since not specified). Certifications not mentioned, so leave blank unless needed. Wait, the user says "Required Education & Certifications". The job description doesn't mention any specific education or certifications. So perhaps write "Bachelor’s degree in Computer Science or related field" for education and specify if certifications are required, but since not mentioned, maybe omit. But the user might expect Education to be included even if not mentioned. Since the original job description doesn't specify, it's safer to leave it as "Bachelor’s degree or higher in a relevant technical field" if necessary, but maybe the user wants to exclude it since it's not mentioned. The instruction says to exclude fluff, so if it's not stated, perhaps omit. Wait, but for Required Education & Certifications, the user may expect that field to be filled. Maybe write "Not specified in the job description" but the user wants it factual. But the instruction says "exclude company details, location, benefits, or fluff", so if education is not mentioned, maybe omit. But in many job postings, education is often included. Since the original is missing it, perhaps leave it as "Not specified" but the user wants the output to be factual. However, in the example provided in the user instruction, how would they handle it? The user's example might not have that, but since the user hasn't given an example, we have to decide. In this case, I'll include "Bachelor's degree in Computer Science or related field" as a standard assumption unless the job description states otherwise. However, since the job description doesn't mention it, perhaps not include it unless the user specifies. But maybe in some cases, it's expected. Hmm. Given the user's instruction to be factual and exclude fluff, I think it's better to omit education unless specified. So for Required Education & Certifications, leave blank or note "Not specified". However, the user's structure expects that section. So perhaps write "Not specified" but the user might prefer it blank. Since the instruction says "Required Education & Certifications", and the job description doesn't mention,
Calgary, Canada
Hybrid
Senior
10-03-2026