---
title: 'Senior Data Scientist at Empirical Security'
canonical: 'https://feeny.ai/job/senior-data-scientist-empirical-security-remote-h65y578hfx54'
type: 'job'
last_seen: '2026-10-15'
---

# Senior Data Scientist at Empirical Security

- **Company:** Empirical Security
- **Location:** Remote
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-08-21
- **Last confirmed live:** 2026-10-15
- **Apply:** https://jobs.ashbyhq.com/empirical-security/f7b15b05-aa9b-4c84-95c5-b5e5917983af

## Job description

Empirical Security is seeking an experienced Security Data Scientist focused on building the next generation of cybersecurity vulnerability models. Our unique approach leverages ground-truth telemetry to develop predictive, actionable insights that transform the way organizations identify, prioritize, and remediate vulnerabilities in cloud, appsec and traditional environments. We build models specific to individual customers, and maintain many of them side by side.

## The role

You own models and data end to end: problem framing, features, training, evaluation, deployment, and the uncomfortable part where you explain to a customer why the vulnerability their board is worried about ranked 400th on the remediation list.

## What you'll do

- Design, train, and ship exploit prediction models against ground-truth exploitation telemetry, in cloud, appsec, and traditional infrastructure.
- Build evaluation that survives contact with reality. Precision, recall, coverage, efficiency, calibration, and how all four decay over time. Accuracy is not a number you report once at launch.
- Solve for extreme class imbalance. A fraction of a percent of published CVEs are ever exploited in the wild, and most of the industry's modeling failures start with pretending that isn't true.
- Work the hard part of the dual-model architecture: partial pooling, hierarchical priors, and cold-start behavior for customers whose local telemetry is thin in month one and rich in month twelve.
- Engineer features across scanner output, EDR, asset inventory, identity, cloud posture, and exploitation telemetry, and be honest about which ones are leakage.
- Own monitoring and drift detection.
- Publish. Papers, methodology write-ups, open benchmarks, conference talks. Our positioning is that we show our work.
- Partner with engineering and our forward deployed team to move models out of notebooks and into production systems that customers depend on.

## What you'll need

- Several years of applied machine learning or statistics with models that ran in production and had consequences when they were wrong.
- Fluency in Python and SQL, and the discipline that comes with version control, reproducible pipelines, and secure handling of customer data.
- Real depth in classification under heavy imbalance, plus at least one of: survival and time-to-event analysis, Bayesian hierarchical modeling, or causal inference.
- Calibration instincts. You should be visibly uncomfortable when a model outputs 0.9 and is right 60% of the time.
- The ability to explain a model to a security executive, and to quantify uncertainty out loud rather than burying it in an appendix.
- Enough curiosity about attacker behavior to ask why a feature works, not just whether it does.

A Final Word Don't check off every box in the requirements listed above? Please apply anyway! Studies have shown that marginalized communities - such as women, LGBTQ+ and people of color - are less likely to apply to jobs unless they meet every single qualification. Empirical Security is dedicated to building an inclusive, diverse, equitable, and accessible workplace that fosters a sense of belonging – so if you're excited about this role but your past experience doesn't align perfectly with every qualification in the job description, we encourage you to still consider submitting an application. You may be just the right candidate for this role or another one of our openings!

## About Empirical Security

## Company Overview
- **One-liner**: Empirical Security builds mathematical models for cybersecurity data, predicting which vulnerabilities are likely to be exploited using both global internet-wide telemetry and enterprise-specific local models.
- **Entity Type**: Private (Seed stage, $11.9M total funding)
- **Headquarters**: Chicago, Illinois, USA
- **Founded**: 2024
- **Founders**: Ed Bellis (CEO), Michael Roytman (Co-Founder), Jay Jacobs (Chief Data Scientist)

## Core Business
- Primary industry: Cybersecurity (Risk-Based Vulnerability Management, Threat Intelligence)
- Target customers: B2B, Enterprise security teams
- Mission: "Build data-driven models to prioritize, remediate, and summarize cybersecurity findings." The future of cybersecurity is data-driven.

## Products & Services
- **Empirical Platform**: Ingests scan results, asset metadata, compensating controls, and network topology to train a local model accessible only to the customer. Provides a web interface, API, and AI agent for triage.
- **Global Models**: Predict the probability of exploitation across 17,000+ CVEs using near-real-time internet exploitation telemetry. Trained on ~2 million exploitation events.
- **Local Models**: Enterprise-specific models trained on an organization’s own environment telemetry and controls to determine which vulnerabilities pose real risk to that specific infrastructure.
- **EPSS Model**: Empirical maintains and trains the Exploit Prediction Scoring System (EPSS), a free and open machine learning model used by over 120 vendors.

## Market Standing
- **Valuation**: Not disclosed
- **Total Funding**: $11.9M (Seed round, August 2025, led by Costanoa Ventures)
- **Key Metric**: 20 employees (9 product + tech); monthly headcount growth of 4.3%
- **Notable Investors/Partners**: Costanoa Ventures; co-chairs the EPSS Special Interest Group at FIRST
- **Growth Signals**: Founded by the team behind Kenna Security (acquired by Cisco). The company maintains the widely adopted EPSS model. Demonstrated higher coverage and efficiency compared to traditional vulnerability management approaches.

## Competitive Advantages
- **Data moat**: Monitors exploitation activity over 17,000+ CVEs (12x DHS CISA’s KEV), with hourly telemetry and years of historical depth.
- **Unique modeling approach**: Combines a global internet-scale model with a local enterprise-specific model, enabling context-aware prioritization that generic models cannot match.
- **Team pedigree**: Founders created the Risk-Based Vulnerability Management category at Kenna Security; Jay Jacobs is lead data scientist for EPSS and co-chair of the EPSS SIG at FIRST.
- **Transparency**: Publishes data to prove model performance; EPSS is free and open.

## Strategic Focus
- Continue building a "new kind of security platform" driven by modern AI and real-world telemetry.
- Expand local model capabilities to cover more use cases (threat intelligence, vulnerability management, application security, cloud security).
- Grow the team and customer base while maintaining open standards leadership.

## Why Work Here
- **Culture**: Described as collaborative, hardworking, and focused on solving new problems with data science and engineering excellence.
- **Work Environment**: Headquarters in Chicago (onsite workspace). Some roles are listed as remote (USA). Likely flexible for the right candidates.
- **Team**: Small (~20 people) and growing, offering significant impact and ownership in an early-stage startup.
- **Engineering Culture**: Rigorous model validation, emphasis on statistical learning systems, and a mission to eliminate noise in security operations. The team includes data scientists, software engineers, and security veterans from Kenna/Cisco.

## Sources
1. [empiricalsecurity.com](https://www.empiricalsecurity.com/)
2. [empiricalsecurity.com/about](https://www.empiricalsecurity.com/about)
3. [builtin.com](https://builtin.com/company/empirical-security)
4. [linkedin.com](https://www.linkedin.com/company/empiricalsecurity)
5. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/empirical-security)

## Other roles at Empirical Security

- [Software Engineer](https://feeny.ai/job/software-engineer-empirical-security-remote-pmwmwp4vx8n4)
- [Commercial Sales Executive](https://feeny.ai/job/commercial-sales-executive-empirical-security-remote-bxhzadwq8kac)
- [Sales Engineer](https://feeny.ai/job/sales-engineer-empirical-security-remote-wnwyn6z8ebfd)
- [Director of Sales Operations](https://feeny.ai/job/director-of-sales-operations-empirical-security-remote-8114maynpvzp)
- [Enterprise Sales Executive](https://feeny.ai/job/enterprise-sales-executive-empirical-security-remote-0q231xw13yt2)
- [Forward Deployed Data Scientist](https://feeny.ai/job/forward-deployed-data-scientist-empirical-security-remote-5a0h29nm6vtk)
- [Empiricist](https://feeny.ai/job/empiricist-empirical-security-remote-1xksk601yqen)
- [Senior Data Scientist](https://feeny.ai/job/senior-data-scientist-headway-new-york-8dek00dkgded) — New York, NY
- [Senior Data Scientist](https://feeny.ai/job/senior-data-scientist-definitive-healthcare-india-bengaluru-g6j47ftjgqsv) — Bengaluru, India
- [Senior Data Scientist](https://feeny.ai/job/senior-data-scientist-probably-genetic-san-francisco-gsgj1tcdngah) — San Francisco, CA