Software Engineer - United Kingdom

As a Software Engineer at DataVisor, you will be integral to the development of our next-generation machine learning platform. Your responsibilities will include designing and building systems that utilize unsupervised and supervised machine learning techniques to detect fraudulent behaviors in real-time, as well as developing large-scale machine learning pipelines using big data technologies.

To be considered for the Software Engineer role at DataVisor, candidates should have 0-3 years of software development experience. Familiarity with Java, Shell, and Python is essential, along with a strong understanding of relational databases and SQL. Experience with big data technologies such as Spark, HBase, and Kafka will enhance your application.

In the Software Engineer role at DataVisor, you will work with a variety of technologies including big data solutions like Spark, Hadoop, HBase, and Cassandra, as well as real-time streaming tools like Kafka and Flink. This experience will greatly enhance your skillset in the rapidly evolving field of machine learning and fraud detection.

DataVisor promotes a flexible working environment, which may include remote working options depending on team collaboration needs and company policies. Always check with the hiring team for the specifics regarding remote work possibilities.

At DataVisor, we pride ourselves on having an open, positive, and collaborative culture. Our team is driven by results—encouraging creativity and communication to foster innovative solutions to fraud detection challenges. You'll find a supportive environment where your ideas are valued!

DataVisor offers a comprehensive benefits package that includes competitive pay, equity participation, health benefits, catered lunches, company off-sites, and fun game nights. We believe in taking care of our team members both professionally and personally.

As a Software Engineer at DataVisor, you'll have ample opportunities for growth. We prioritize continuous learning and development, providing access to various training resources and the chance to work on impactful projects that push your career forward in the fields of AI and fraud detection.

Unsupervised machine learning is a method that allows systems to learn from data without labeled outcomes. In fraud detection, it's crucial because it can identify patterns and anomalies in data that indicate fraudulent activity, often before a human could recognize them.

When designing a scalable machine learning system, I prioritize modular architecture, use big data technologies like Spark for processing large datasets, and ensure the system can accommodate real-time data streams to enhance responsiveness and accuracy in detecting fraud.

I am proficient in Java, Python, and Shell. In past projects, I've utilized Python for data analysis, Java for building scalable services, and Shell scripting for automating data workflows, which helped improve efficiency in our systems.

In a previous role, I worked on a project that required integrating a complex machine learning model into an existing system. The main challenge was ensuring the model could handle large volumes of data in real-time. I collaborated closely with data engineers to optimize the data flow and performance, ultimately delivering a successful integration.

I have hands-on experience with big data technologies, including Apache Spark and Kafka. For instance, I used Spark to build a data processing pipeline that allowed our team to analyze large datasets efficiently, which significantly sped up our fraud detection processes.

I focus on test-driven development, writing unit and integration tests to ensure code quality. Additionally, I participate in code reviews to gather feedback from colleagues, which helps catch potential issues early and improves overall codebase quality.

Absolutely! In a previous project, we built a model that analyzed transaction patterns. Using unsupervised learning methods, the model identified anomalies that indicated potential fraud. We then flagged these transactions for further investigation, which helped reduce fraudulent activities.

A real-time processing system ingests and analyzes data as it arrives, allowing for immediate decision-making. This is critical in fraud detection, as it allows organizations to respond quickly to suspicious activities, reducing potential losses.

I believe in open communication and regular collaboration with my team members. By sharing ideas and strategies, we can better troubleshoot problems and innovate solutions. I actively participate in team meetings and encourage knowledge sharing among colleagues.

Fraud detection fascinates me because it combines cutting-edge technology with real-world impact. The challenge of staying ahead of fraudsters by leveraging machine learning and data analytics to protect consumers and businesses is incredibly motivating to me.