---
title: 'Forward Deployed Data Scientist at Empirical Security'
canonical: 'https://feeny.ai/job/forward-deployed-data-scientist-empirical-security-remote-5a0h29nm6vtk'
type: 'job'
last_seen: '2026-10-15'
---

# Forward Deployed Data Scientist at Empirical Security

- **Company:** Empirical Security
- **Location:** Remote
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-01-12
- **Last confirmed live:** 2026-10-15
- **Apply:** https://jobs.ashbyhq.com/empirical-security/99125a1c-ea07-499e-a056-5ae0848f58b7

## Job description

Forward Deployed Data Scientist

Empirical Security is seeking an experienced Security Data Scientist to join our innovative Forward Deployed Data Scientist (FDDS) team 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.

This hasn’t been done before in cybersecurity. Come change the way security teams make decisions with us. FDDS works side by side with our customers, rapidly understanding their toughest issues; architecting and building solutions that get the right data into the hands of modeling data scientists and providing insights to our design partners and customers.

Familiarity with complex cybersecurity environments and data sets is a plus here.

What You’ll Do:

- Collaborate closely with our customers, engineering, product, and security teams to operationalize vulnerability models, ensuring scalability, reliability, and alignment with customer needs.
- Lead discovery and prioritization of customer security data sources (asset inventory, vuln scanners, EDR, IAM, CMDB, cloud posture, ticketing, external attack surface, threat intel), including feasibility, value, and effort trade-offs.
- Apply exposure-management domain expertise to ensure data supports actionable use cases (attack surface reduction, vulnerability prioritization, remediation workflows, risk acceptance, SLA tracking).
- Partner with engineering to design and validate ingestion pipelines (APIs, exports, streaming/batch), ensuring reliability, observability, and secure handling of customer data.
- Perform pragmatic data analysis to diagnose data issues and quantify impact (completeness, accuracy, timeliness, consistency), and recommend remediation steps to customers and internal teams.
- Define and maintain customer-facing technical documentation: integration guides, data dictionaries, validation checklists, and runbooks for common ingestion and modeling issues.
- Collect, clean, explore, analyze, and normalize various security data sources.
- Stay current on exposure-management practices, vulnerability intelligence, attacker tradecraft, and the relevant vendor ecosystem to inform integrations and customer guidance.

What You’ll Need:

- Baseline engineering hygiene (Python/SQL comfort, APIs and data formats, Git/version control, and an appreciation for reliability/observability and secure data handling).
- Enterprise security engineering / architecture fluency (security controls, reference architectures, trade-offs, and how security capabilities integrate into real-world enterprise environments).
- Exposure and vulnerability management expertise (asset-centric thinking, prioritization workflows, remediation SLAs, exception handling, and common program maturity patterns).
- Security data integration and normalization skills (ability to evaluate customer data sources, assess data quality, define mapping/normalization, and drive onboarding priorities).
- Strong customer-facing technical communication (requirements discovery, explaining complex technical concepts clearly, running workshops, and producing crisp technical documentation).
- Working knowledge of common security telemetry and systems (e.g., vulnerability scanners, EDR, IAM, CMDB, ticketing/ITSM, cloud security, external attack surface—enough to ask the right questions and validate data fitness).
- Pragmatic analytics capability (comfortable with basic statistics, exploratory analysis, and sanity-checking model outputs; can quantify uncertainty and limitations without being a deep ML specialist).
- Technical collaboration across engineering and data science (can translate customer needs into technical requirements, partner on pipeline design, and unblock implementation details).

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)

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