Data Science Manager London, UK • Data & Analytics • Data Science +1 more London, UK Data & Ana[...]

Meta
London
3 months ago
Applications closed

Related Jobs

View all jobs

Hybrid Senior Manager, Audit Data Analytics & AI

Business Intelligence Manager - 12Month Maternity Cover

Business Intelligence Manager

Head of Data Analytics & AI

Financing Quantitative Analyst

Data Analyst Placement Programme

As a Data Science Manager at Meta, you will help shape the future of the experiences we build for billions of people and hundreds of millions of businesses, creators, and partners around the world. You will apply your people leadership, project management, analytical, and technical skills, creativity, and product intuition to one of the largest data sets in the world. You will collaborate on a wide array of product and business problems with a wide range of cross-functional partners across Product, Engineering, Research, Data Engineering, Marketing, Sales, Finance, and others. You will influence product strategy and investment decisions with data, be focused on impact, and lead and grow an impact-oriented team. By joining Meta, you will become part of the analytics community dedicated to skill development and career growth in analytics and beyond.

About the role:

Product leadership: You will use data to understand the product and business ecosystem, quantify new opportunities, identify upcoming challenges, and shape product development to bring value to people, businesses, and Meta. You will help develop strategy and support leadership in prioritizing what to build and setting goals for execution.

Analytics: You will guide product teams using data and insights. You will focus on developing hypotheses and employ a varied toolkit of rigorous analytical approaches, different methodologies, frameworks, and technical approaches to test them.

Communication and influence: You won’t simply present data, but tell data-driven stories. You will convince and influence leaders using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner.

People leadership: You will inspire, lead, and grow a team of data scientists and data science leaders.

Data Science Manager Responsibilities

  1. Lead a team of data scientists to develop strategies for our products that serve billions of people and hundreds of millions of businesses, creators, and partners around the world.
  2. Drive analytics projects end-to-end in partnership with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions.
  3. Influence product direction through clear and compelling presentations to leadership.
  4. Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches.
  5. Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends.
  6. Define, understand, and test opportunities and levers to improve the product, and drive roadmaps through your insights and recommendations.
  7. Contribute towards advancing the Data Science discipline at Meta, including but not limited to driving data best practices (e.g. analysis, goaling, experimentation), improving analytical processes, scaling knowledge and tools, and mentoring other data scientists.

Minimum Qualifications

  1. Currently has, or is in the process of obtaining, a Bachelor's degree or equivalent practical experience. Degree ideally should be completed before joining Meta.
  2. A minimum of 4 years of work experience (2+ years with a Ph.D.) in applied analytics, including a minimum of 2 years of experience managing analytics teams.
  3. Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R).
  4. Experience initiating and completing analytical projects with minimal guidance.
  5. Experience communicating results of analysis to leadership.

Preferred Qualifications

  1. Master’s or Ph.D. degree in Mathematics, Statistics, Computer Science, Engineering, Economics, or another quantitative field.
  2. Experience working in technology, consulting, or finance.
  3. Proven track record of leading impact-oriented analytics teams.

About Meta

Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram, and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.

Meta is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics.

Meta is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, fill out the Accommodations request form.

Apply for this job. Take the first step toward a rewarding career at Meta.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.