Head of Data Science

Location: London - Commercial

Department: Data Research and Insights

Role Title: Head of Data Science

Location: UK-based (with support from India-based teams)

Reports to: VP Insights & Decision Science 

Team: 6 Data Scientists (split between Decision Science and Personalisation)

PURPOSE OF ROLE

The Head of Data Science is a high-impact leadership position responsible for building and leading a world-class "decision engine" team. As a key architect of our "AI-powered future," you will accelerate the transformation The Economist’s Data Science team from a service provider into a "trusted adviser" that delivers commercially transformative advice and world-class personalisation capabilities

You will be responsible for setting and raising the technical and operational standards of the team, fostering a culture of technical excellence and innovation. 

Your remit covers two critical pillars:

1. Decision Science: Building the "muscle" for causal inference and advanced forecasting to support high-stakes strategic decisions e.g. understanding the relationship between subscriber behaviour/engagement and retention/value, marketing and media optimisation, understanding the drivers of content performance, pricing and discounting strategy, customer lifetime value modeling, etc.
2. Personalisation: Rapidly maturing our recommendation and pricing engines to drive improvements in subscriber acquisition, engagement, retention and lifetime value metrics
   
   

MEASURES OF SUCCESS

Qualitative Measures:

• Culture of Excellence: Recognition as a "torch-bearer" for excellence who sets and consistently meets the highest standards in quality, pace, and expertise.
• Talent Development: Evidence of nurturing a high-performance team with a clear pipeline of talent and technical growth.
• Scaling & Reliability: Implementation, in collaboration with the Engineering team, of robust build, MLOps and architectural standards that enable rapid experimentation, build and deployment cycles and that ensure model reliability, observability, and reusability
• Trusted Adviser Status: The extent to which senior business and technical stakeholders proactively seek your team’s expertise for complex technical and strategic questions.

Quantitative Measures:

• Material Commercial Impact: Quantifiable and material net revenue growth and operational savings directly attributable to technical innovations (e.g., pricing models, personalization uplift).
• Model Performance & Velocity: Significant improvement in the speed of model development/deployment and the accuracy of causal models/diagnostics.
• Adoption & Engagement: High levels of integration and usage of data science products across the organization’s core workflows and experiences
  
  

ROLE RESPONSIBILITIES

• Team Leadership & Talent Nurturing: Lead, mentor, and develop a high-performance team of ~6 Data Scientists. You will be accountable for their technical growth and for maintaining a "T-shaped" culture that combines both broad and deep technical/business expertise.
• Technical Standards & MLOps: Own the technical architecture and MLOps lifecycle for data science. In collaboration with the Data Engineering and AI Platform teams, you will drive excellence and pace in the build, deployment, testing, and monitoring of models using Amazon Sagemaker [and occasionally Snowflake]
• Causal Inference & Decision Science: Lead the development of advanced causal models (e.g., Media Mix Modelling, retention drivers, and simulation models) to move the business from descriptive "what happened" to prescriptive ‘what next” and "what if" insights.
• Personalisation Strategy & Activation: Oversee the Personalisation Analysts in their close collaboration with Marketing and Product teams to identify and execute opportunities using our CDP and activation platforms (Salesforce, Airship, Blueconic and Amplitude).
• NLP & Generative AI Innovation: Leverage NLP and transformer architectures to enhance content tagging and use Generative AI to supercharge internal AIML workflows, including model testing and documentation.
• Stakeholder Consultancy: Act as a senior technical consultant to executive fora, translating complex technical findings into compelling, actionable narratives.
• Democratising AI & ML: Driving adoption of AI & ML techniques and tools in the wider Data, Research & Insight team and in the wider business
  
  

CANDIDATE PROFILE

Must-Have Experience & Expertise:

• Proven Leadership: A track record of building and raising standards within high-performance data science teams, with a demonstrable focus on talent development.
• Technical Innovation with ROI: A proven record of delivering technical innovations that have resulted in quantifiable and material commercial benefits.
• Curiosity with Purpose: A restless intellect that is constantly seeking to grow their skills and knowledge and, crucially, an operational and practical mindset that finds ways to apply that knowledge to deliver commercial benefits
• Decision Science & Causal Inference: Deep expertise in causal inference, forecasting, and simulation techniques used to support business decision-making and to develop commercial and product strategy 
• Personalised User Experiences & Journeys : Sustained track record of delivering performant and innovative AI & ML models that result in enhanced subscriber experience and commercial performance improvement through content recommendations, product recommendations, personalised pricing and customer journey orchestration
• Engineering Excellence: Strong experience in MLOps, model architecture, and delivering models at scale using AWS/Sagemaker.
• Modern AI Stack: Hands-on experience with NLP, neural networks, transformer architectures, causal inference and the application of Generative AI in the AIML lifecycle.
• Commercial Agency: An "owner's mindset" with the bravery to find and fix problems proactively and a focus on opportunity over risk.

Desirable Experience & Expertise:

• Subscription/Journalistic Context: Experience in a premium news or subscription-based environment, understanding the specific challenges of content-based engagement.
• Activation Platforms: Familiarity with activation via CDPs (e.g. Salesforce, Airship, Blueconic) and product analytics tools (e.g. Amplitude).

AI Transformation: Experience in evolving a traditional analytics function into an AI-forward team that leverages "full-stack" capabilities.

#LI-Hybrid