Storyful is an equal opportunity employer

Job Description :

Job Title : Lead Data Scientist
Location: Dublin (Hybrid — 3 days/week in office)

Reporting to: CPTO (Storyful)

About Storyful:

Storyful (a News Corp company) helps organisations discover, verify, and act on information at speed — from breaking news to complex reputation and narrative risk.

We’re building RiskRadar: an AI-native, decision-ready Narrative & Reputational Risk platform that fuses signals from licensed content, social, and broadcast/video into explainable scoring and clear next actions for high-scrutiny brands — including financial services buyers with serious governance expectations.

This is a pivotal hire: the first Data Scientist at Storyful. You’ll set the bar for how we build trustworthy ML/AI that ships.

Role Mission

Build and production is the core data science capabilities behind RiskRadar — specifically:

a robust, explainable Reputation / Narrative Risk scoring system, and
entity resolution across messy, multi-source datasets (brands, products, execs, organisations, locations, subsidiaries).

Bonus (strategic): help us explore vision + multimodal models to detect manipulated / misleading video for our Newswire verification workflows.

What You’ll Do (Hands-on Responsibilities)

1) Reputation & Narrative Risk Scoring (Explainable + Governed)

Design a score framework (overall + sub-scores) that is measurable over time, resistant to gaming, and useful in executive decision-making.
Build scoring that can answer: “What changed, why, and what evidence supports this?”
Implement calibration, confidence, and uncertainty so customers understand when to trust the model and when to escalate to humans.
Set up model governance patterns aligned to model risk management expectations (audit trails, versioning, monitoring, documentation, change control).

2) Entity Resolution (Truth Layer Across Sources)

Build entity resolution pipelines that unify entities across licensed news, social, and broadcast/video metadata.
Combine the right approaches: rules where they win, probabilistic matching where it matters, embeddings/LLM-assisted linking where it scales — always with measurable quality.
Establish golden datasets, error taxonomies, and repeatable evaluation so entity resolution improves continuously.

3) Ship ML Like a SaaS Operator (Fast Experiments → Production)

Run crisp experiments: hypotheses, baselines, metrics, iteration loops — and kill weak ideas early.
Partner with Product + Engineering to take ML/AI features to production quickly, safely, and cost-effectively.
Build evaluation harnesses for ML and LLM components (offline + human review + online measurement).
Implement production standards: monitoring, drift detection, cost/latency controls, incident playbooks, and quality dashboards.

4) Founding DS Leadership (Today: IC; Soon: Build the Team)

Establish best practices for experimentation, reproducibility, documentation, and responsible AI.
Lead cross-functional delivery with Product, Engineering, and AI teams across News Corp.
Within 12 months: help hire/mentor 1–2 additional DS/ML roles (scope dependent), while staying hands-on.

5) Bonus: Video Verification ML (Vision / Multimodal)

Prototype approaches for manipulated media detection and video authenticity signals.
Translate “model output” into a verification workflow with clear confidence and evidence, not black-box answers.

Tech & Working Environment

Cloud: AWS
LLM stack: LangChain (or equivalent patterns), Langfuse for tracing/observability, modern LLM APIs
Core: Python, SQL, data pipelines, model packaging + CI/CD
You’ll work closely with our AI Architect, Product, Engineering, and verification experts.

What You’ll Bring (Requirements)

Must-have

7+ years in applied Data Science / ML, with multiple production deployments in a commercial environment (SaaS strongly preferred).
Proven experience leading cross-functional teams to deliver production-ready ML/AI (even if you weren’t the people manager).
Strong grounding in: classification/scoring/ranking, NLP (and/or LLM applications), statistics, evaluation, and experimentation.
Demonstrated ability to build explainable systems: not just performance, but transparency, evidence, and user trust.
Experience designing evaluation strategies: labeled datasets, human-in-the-loop review, acceptance thresholds, monitoring and drift.
Comfortable operating in ambiguity with high ownership and high pace.

Strong advantage

Deep experience with entity resolution / record linkage at scale (probabilistic matching, embeddings, graph-based approaches).
Experience building for regulated / high-governance contexts (financial services is a plus): auditability, documentation, controls.
LLM evaluation and reliability methods (prompt eval, retrieval eval, hallucination mitigation, guardrails).
Computer vision / multimodal experience, especially around authenticity, manipulation detection, or media forensics.

What Success Looks Like

First 90 days

Clear score and entity resolution strategy, baselines, datasets, and metrics agreed with Product/Engineering.
First production-ready scoring and entity resolution increments shipped behind feature flags.
Evaluation + monitoring foundations in place (including Langfuse tracing standards for LLM workflows).

By 6 months

Explainable scoring system with evidence trails, confidence, and drift monitoring live for real users.
Entity resolution quality improving on a measurable cadence (golden set + error reduction plan).
Governance pack in place suitable for financial services buyers (documentation, audit trails, change controls).

By 12 months

Mature experimentation-to-production loop: faster iteration, lower incident rate, clearer model performance visibility.
You’ve begun mentoring/hiring to expand DS capability while remaining a hands-on technical leader.
Optional: early vision/multimodal verification prototypes validated with newsroom workflows.

Why This Role is Different

You’re not inheriting a mature DS org — you’re founding it.
You’ll build AI that executives will rely on under pressure — where explainability and governance aren’t “nice to have”, they’re the product.
You’ll ship. A lot. And you’ll help define what “good” looks like for Storyful’s AI future.

Job Category:

Storyful - Product & Technology