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 in Meta Business Suite. This role will be responsible for helping the team develop products such as our business and creator classifiers, unified business graph, as well as recommendation systems for our business tools.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 Machine Learning knowledge, data and analysis to identify and solve product development’s biggest challenges. 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, data science, machine learning and beyond.About the roleProduct 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 diverse toolkit of rigorous analytical approaches, different methodologies, frameworks, and technical approaches including ML to test them. You will research challenging ML questions to inform experimentation and ML development.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.
Data Scientist, Machine Learning – Product Analytics Responsibilities:
Lead the analytical approach and best practices across ranking sub-pillar, coaching members of the team.
Establish and leverage partnerships with broader ML teams across Meta to find collaboration opportunities and exchange best practices.
Lead 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.
Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches.
Apply technical expertise with machine learning, quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for our products that serve billions of people and hundreds of millions of businesses.
Partner with cross-functional engineering and product teams to derive quantitative understanding of Meta’s ML infrastructure and ML applications.
Inform direction and strategic decisions for the future of ML and large-scale distributed systems at Meta.
Identify opportunities and develop solutions in existing large scale distributed systems and ML stack.
Define, understand, and test opportunities and levers to improve the product through ML models and applications, and drive ML-modeling roadmaps through your insights and recommendations.
Contribute towards advancing the Data Science discipline at Meta, including but not limited to driving data best practices (e.g. analysis, goaling, experimentation, machine learning), improving analytical processes, scaling knowledge and tools, and mentoring other data scientists.
Bachelor’s degree in Mathematics, Statistics, a relevant technical field, or equivalent practical experience.
A minimum of 4 years of experience (2+ years with a Ph.D.) in one or more of the following: ML modeling, Deep Learning, Ranking, Recommendation, or Personalization systems.
Experience with statistical data analysis such as linear models, multivariate analysis, stochastic models, and sampling methods.
Experience with applying machine learning techniques to big data systems (e.g., Spark and Hadoop) with TB to PB scale datasets.
Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R).
Ph.D. in a quantitative field.
Strong research record demonstrated through publications.
Knowledge of one or more of advanced ML techniques such as Classification, Clustering, Prediction, Recommender Systems, Time Series Forecasting, Anomaly Detection, and Privacy Preserving Machine Learning.
Experience in applied machine learning in two-sided marketplaces.
Level of experience (years):
Mid Career (2+ years of experience)