As a Machine Learning Data Scientist at Meta, you will have the opportunity to do groundbreaking applied machine learning work that will shape the industry and the future of people-facing and business-facing products we build across our entire family of applications (Facebook, Instagram, Messenger, WhatsApp, Reality Labs). By applying your Machine Learning knowledge and technical skills, analytical mindset, and product intuition to one of the richest data sets in the world, you will help define the experiences we build for billions of people and hundreds of millions of businesses around the world. You will collaborate on a wide array of product and business problems with a diverse set of cross-functional partners across Product, Engineering, Research, Data Engineering, Marketing, Sales, Finance and others. You will use data and analysis to identify and solve product development’s biggest challenges in ML systems through insights as well as prototyping ML solutions. You will influence product strategy and investment decisions with data, be focused on impact, and collaborate with other teams. By joining Meta, you will become part of a world-class analytics community dedicated to skill development and career growth in analytics and beyond. In contrast to most ML engineering roles, ML in product analytics allows you to work out ML solutions for broader less defined problems where you can use not just ML knowledge but also strong analytical skills to break down complex problems into well-learnable parts. Product leadership: You will use data to shape product development, quantify new opportunities, identify upcoming challenges, and ensure the products we build bring value to people, businesses, and Meta. You will help your partner teams prioritize what to build, set goals, and understand their product’s ecosystem. Analytics: You will guide teams using data and insights. You will focus on developing hypotheses and employ a diverse toolkit of rigorous analytical approaches, different methodologies, frameworks, and technical approaches including Machine Learning to test them. You will research challenging ML questions to inform experimentation and can build ML prototypes. . Communication and influence: You won’t simply present data, but tell data-driven stories. You will convince and influence your partners using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner.