At a Glance
- Tasks: Lead a high-performance data science team and drive innovative AI solutions.
- Company: Join a prestigious organisation transforming data into actionable insights.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Dynamic role with a focus on talent development and technical excellence.
- Why this job: Be at the forefront of AI innovation and make a real impact on business strategies.
- Qualifications: Proven leadership in data science and expertise in causal inference and machine learning.
The predicted salary is between 80000 - 100000 € per year.
Requirements
- 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) Subscription/Journalistic Context: Experience in a premium news or subscription-based environment, understanding the specific challenges of content-based engagement.
- (Desirable) Activation Platforms: Familiarity with activation via CDPs (e.g. Salesforce, Airship, Blueconic) and product analytics tools (e.g. Amplitude).
- (Desirable) AI Transformation: Experience in evolving a traditional analytics function into an AI-forward team that leverages "full-stack" capabilities.
What the job involves
- Reports to: VP Insights & Decision Science.
- Team: 6 Data Scientists (split between Decision Science and Personalisation).
- 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:
- 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 modelling, etc.
- 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, personalisation 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 organisation's core workflows and experiences.
- 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.
Head of Data Science in London employer: Deepstreamtech
At The Economist, we pride ourselves on being an exceptional employer that fosters a culture of innovation and excellence. As the Head of Data Science, you will lead a high-performance team in a dynamic environment that prioritises talent development and technical growth, while also delivering impactful commercial results. Our commitment to employee well-being, coupled with our focus on cutting-edge AI technologies, ensures that you will thrive both personally and professionally in this pivotal role.
StudySmarter Expert Advice🤫
We think this is how you could land Head of Data Science in London
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the data science community. Attend meetups, webinars, or even just grab a coffee with someone in the field. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio that highlights your best projects, especially those that demonstrate your expertise in causal inference and personalisation strategies. Share it on platforms like GitHub or LinkedIn to catch the eye of potential employers.
✨Tip Number 3
Prepare for interviews by diving deep into the company’s data practices. Understand their challenges and think about how your experience with MLOps and AI transformation can help them. Tailor your responses to show how you can be a trusted adviser in their journey.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive and engaged with our mission at StudySmarter.
We think you need these skills to ace Head of Data Science in London
Some tips for your application 🫡
Show Your Leadership Skills:When writing your application, make sure to highlight your experience in building and leading high-performance data science teams. We want to see how you've developed talent and raised standards in your previous roles.
Demonstrate Technical Innovation:Share specific examples of technical innovations you've delivered that resulted in tangible commercial benefits. We love seeing how your work has made a real impact, so don't hold back on the details!
Emphasise Curiosity and Practicality:Let us know about your continuous learning journey and how you apply your knowledge practically. We're looking for someone with a restless intellect who can turn insights into actionable strategies.
Tailor Your Application:Make sure your application speaks directly to the job description. Use relevant keywords and phrases that align with our needs. And remember, applying through our website is the best way to get noticed!
How to prepare for a job interview at Deepstreamtech
✨Showcase Your Leadership Skills
Make sure to highlight your experience in building and leading high-performance data science teams. Share specific examples of how you've developed talent and raised standards within your previous teams, as this role requires a strong focus on leadership.
✨Demonstrate Technical Innovation
Prepare to discuss your past technical innovations that have led to measurable commercial benefits. Be ready to provide concrete examples of how your work has directly impacted revenue or operational efficiency, as this will resonate well with the interviewers.
✨Exhibit Curiosity and Practical Application
Show your passion for continuous learning and how you apply new knowledge to solve real-world problems. Discuss any recent skills or techniques you've acquired and how they can be leveraged to enhance decision-making and personalisation strategies.
✨Prepare for Causal Inference Discussions
Given the emphasis on decision science and causal inference in this role, brush up on your understanding of these concepts. Be prepared to explain how you've used forecasting and simulation techniques to support business decisions in your previous roles.
