Okay, I've searched for Data Scientist positions in the Greater Chicago Area, full-time roles within the cybersecurity industry, requiring skills in machine learning and Python, and targeting mid to senior level experience. I have found 56 positions for you.

Sure. Here's the analysis:

Job Analysis:

This Data Scientist role at Empirical Security is fundamentally centered on pioneering predictive cybersecurity vulnerability models that transform abstract telemetry data into actionable business insights. The candidate is expected to develop and continually refine statistical and machine learning models that predict vulnerability exploitation likelihood, timing, and impact, while managing the unique challenge of tailoring models to individual customers simultaneously—a novel and complex task in cybersecurity. Beyond technical proficiency, success here requires a scientific rigor for accuracy, reproducibility, and reliability given the noisy and incomplete data typical in security telemetry. The role demands not only deep expertise in scalable Python coding, big data, and cloud environments but also strong collaboration skills to operationalize models effectively across engineering, product, and security teams. The ability to communicate complex insights clearly to diverse audiences and mentor junior team members is vital, underscoring an expectation of leadership within a highly innovative yet challenging environment. The candidate will frequently confront ambiguity in data quality and emerging cyber threats, requiring judgment to balance empirical evidence with model assumptions. Measuring success in the first 6–12 months likely revolves around delivering robust, validated models that improve actionable prioritization of vulnerabilities for customers and integrating these models into scalable, reliable systems aligned with business needs.

Company Analysis:

Empirical Security operates as a highly specialized, innovation-driven player in the cybersecurity domain, uniquely positioning itself as a developer of both global and personalized mathematical models for security data. This dual focus on broad applicability and tailored, customer-specific threat assessments places Empirical at the cutting edge of cybersecurity modeling, indicating a culture that values innovation, technical excellence, and agility. The organization’s approach to diversity and inclusion suggests a progressive, equitable workplace that prioritizes belonging and varied perspectives, valuable in solving complex cybersecurity problems. The candidate should expect a fast-paced, intellectually demanding environment where continuous learning is encouraged, especially given the evolving nature of cyber threats and data science techniques. This role likely sits at a pivotal junction connecting data science, engineering, product, and security leadership—offering considerable visibility and influence. Strategically, the hire is crucial for scaling Empirical’s unique modeling capabilities and advancing its mission to revolutionize vulnerability management through predictive analytics. Therefore, alignment with Empirical’s culture of rigor, innovation, and inclusivity will be key to thriving long-term.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you had to develop a predictive model using noisy or incomplete data. How did you ensure accuracy and reliability?
• Behavioral: Describe an experience where you collaborated with cross-functional teams to operationalize a data science solution. What challenges did you face and how did you overcome them?
• Behavioral: Share an instance when you had to explain complex technical concepts to a non-technical audience. How did you ensure your message was clear and actionable?
• Behavioral: Can you discuss a situation where you mentored junior colleagues or led a technical initiative?
• Technical: How would you approach building and validating a machine learning model to predict vulnerability exploitation time given telemetry data?
• Technical: What experience do you have with cloud-based scalable data pipelines, and how would you implement one tailored for large-scale cybersecurity data?
• Technical: Which statistical techniques and performance metrics would you prioritize to evaluate and improve predictive models in cybersecurity?
• Technical: Describe your experience with version control (e.g., Git) and software development best practices in data science projects.
• Situational: Imagine a key telemetry data source used for your models has suddenly degraded in quality. How would you identify the issue and mitigate its impact on your models?
• Situational: You are asked to develop a vulnerability model for a new technology stack that you have limited prior data on. What steps would you take to produce a reliable model?
• Situational: Suppose your model’s predictive output shows high uncertainty. How would you communicate this to internal stakeholders and end customers while maintaining credibility?
• Company Fit: What excites you most about applying data science to cybersecurity, and why at Empirical Security?
• Company Fit: How do you see your personal values aligning with Empirical Security’s commitment to inclusivity and innovation?
• Company Fit: Considering Empirical’s focus on customized models for customers, how would you prioritize competing demands from multiple client projects?
• Questions for Interviewer: Can you describe the current team structure around data science and how collaborative initiatives are facilitated across departments?
• Questions for Interviewer: What are the biggest challenges Empirical faces in operationalizing its predictive vulnerability models at scale?
• Questions for Interviewer: How does Empirical support continuous learning and staying current with emerging cybersecurity trends and modeling techniques?