Data Scientist, Analytics (Technical Leadership)

From the original post

Position Overview Meta is on the lookout for experienced Data Scientists to enhance their analytics team. This role is pivotal for driving impact across various product areas. The selected candidates will work closely with cross-functional partners to identify and tackle complex problems using data and analysis. The primary goal of this position is to shape product development, quantify new opportunities, and ensure that the products developed add value to both users and the company. Key Responsibilities The responsibilities for the Data Scientist, Analytics (Technical Leadership) position include: Working with complex data sets to address challenging problems through analytical and statistical methodologies. Applying technical expertise in quantitative analysis, experimentation, and data mining, paving the way for effective product strategies. Setting goals and monitoring key metrics to measure success. Collaborating with cross-functional teams to influence product strategy and investment decisions. Defining and developing the long-term vision and strategy for various programs and products. Partnering with executives to establish data platforms and instrumentation. Presenting insights and recommendations to stakeholders effectively. Supporting the creation of roadmaps and coordinating analytics efforts across different teams. Required Skills and Qualifications Candidates interested in this position must meet the following qualifications: A Bachelor's degree in Computer Science, Computer Engineering, or a related technical field (or equivalent practical experience). At least 5 years of experience with data querying languages such as SQL, as well as knowledge of scripting languages (e.g., Python) or statistical/mathematical software (e.g., R, SAS, Matlab). A minimum of 8 years of experience leading analytics work within an individual contributor capacity, collaborating with Engineering teams, and guiding data-driven product planning, prioritization, and strategy development. Proficiency in handling complicated analytics projects involving multiple stakeholders and cross-functional teams including Engineering, PM/TPM, Analytics, and Finance. Expertise in predictive modeling, machine learning, and experimentation/causal inference methods. Demonstrated capability to communicate complex technical topics clearly, accurately, and in an actionable manner. A minimum of 10 years of experience doing so with leadership teams to influence strategy is required. An advanced degree, either a Master's or Ph.D., in a quantitative discipline such as Mathematics, Statistics, or Operations Research is preferred. A proven track record of executing detailed quantitative analysis in product analytics over 10 years. Familiarity with integrating AI tools to optimize and redesign workflows, driving measurable impacts such as efficiency gains or quality enhancements. Experience in implementing responsible and ethical AI practices, including risk assessment and bias mitigation. A commitment to ongoing AI skill development, which includes keeping up-to-date with emerging AI technologies and methodologies. Salary Details While specific salary information is not provided in the posting, positions at Meta, particularly those at the level of Data Scientist within a technical leadership role, often come with competitive compensation packages, including base salary, bonuses, and potential equity options. Candidates are encouraged to negotiate salary based on their experience and expertise in similar roles. Company Overview Meta is a leading technology giant known for its innovative products and commitment to building community. With a focus on analytics and leveraging data science, Meta encourages skill development and career progression within its diverse team. Conclusion This role at Meta presents an enticing opportunity for seasoned Data Scientists to lead impactful projects and engage in data-driven product development. Ideal candidates will possess a solid combination of technical skills, analytical capabilities, and leadership experience. The chance to become part of a world-class analytics community, dedicated to personal and professional growth, stands as a notable incentive for anyone looking to advance their career in data science.