Data Scientist

At Fable Security, a Data Scientist plays a crucial role in developing and scaling AI-driven security products. Your primary responsibilities include expanding our risk analysis engine, analyzing APIs for emerging data trends, and utilizing behavioral analytics to enhance security outcomes. You'll also implement machine learning models and work cross-functionally to drive innovation, making this position pivotal to our mission.

To qualify for the Data Scientist role at Fable Security, candidates must have at least 5 years of experience in data science or AI-driven product development. Proficiency in Python and SQL, along with expertise in ML frameworks such as TensorFlow or PyTorch, is critical. A strong background in predictive modeling and behavioral analytics is also important, while experience in cybersecurity or risk modeling is considered a plus.

Fable Security integrates AI at the core of its products, and as a Data Scientist, you will leverage cutting-edge AI tools to enhance data products, generate threat intelligence, and develop predictive security insights. Your work with machine learning models will be vital in personalizing security interventions and improving overall risk assessment, making AI a key component of your responsibilities.

As a Data Scientist at Fable Security, you'll be part of a small and fast-moving team that includes co-founders with strong data science backgrounds, engineers, and product managers. Collaboration is key in our San Francisco office, where you'll work closely with cross-functional teams to foster innovation and drive impactful decisions based on data insights.

Fable Security is experiencing explosive growth, and as the first Data Scientist in a rapidly scaling startup, there are numerous opportunities for career advancement. You'll have a chance to shape the future of our security platform, influence product development, and collaborate directly with leadership, which can lead to significant professional growth in your data science career.

To expand the risk analysis engine, I would start by analyzing existing data sources and APIs to identify additional metrics that could indicate employee risk. Next, I would apply behavioral analytics to detect trends and patterns while collaborating with product managers to ensure our enhancements align with user needs and business objectives.

I am experienced with TensorFlow and PyTorch. I prefer TensorFlow for its powerful production capabilities and scalability, while I appreciate PyTorch for its usability in research and ease of prototyping. Both frameworks have strengths that make them suitable for different stages of model development.

In my previous role, I developed a predictive model that assessed customer behavior data to identify fraud risks. By analyzing transaction patterns using machine learning techniques, I successfully reduced false positives and improved response to potential threats.

I begin by engaging with stakeholders to fully understand their challenges and objectives. Then, I analyze available data to determine how it can address these issues. Finally, I apply data science techniques to develop tailored solutions that meet business needs and track their impact.

I typically employ supervised and unsupervised learning techniques for anomaly detection, including clustering algorithms and statistical methods. I also use neural networks for complex data patterns. Regularly testing and validating these models with real-world scenarios is crucial to ensure accuracy.

I make it a priority to follow thought leaders on platforms like LinkedIn and Twitter, participate in relevant online communities, and attend industry conferences. Additionally, I regularly read research papers and articles from journals to stay informed about emerging developments in data science and cybersecurity.

I worked on a project with a cross-functional team of product managers, engineers, and marketing specialists. My role involved analyzing consumer data to provide insights that shaped product features. By collaborating closely, we ensured that the decisions made were data-driven and aligned with market trends.

One challenge I've faced is ensuring that the model's performance remains consistent across different environments. To address this, I put robust testing and validation procedures in place before deployment, alongside continuous monitoring post-deployment to quickly identify and address any issues that arise.

I would analyze employee behavior data to identify patterns that precede security breaches. By developing targeted interventions based on this analysis, we can create personalized security training and alerts, ultimately reducing risk and enhancing overall security outcomes.

I'm motivated by the need to protect individuals and organizations from evolving cyber threats. The dynamic nature of cybersecurity, coupled with the challenge of addressing human behavior in security, drives my passion to innovate and develop solutions that enhance safety and reliability in the digital realm.