• You are inspired by our mission to place one billion people in dream jobs
• You are fully aligned with our values
• You have in-depth experience in creating and maintaining recommenders and/or predictive algorithms for 2-sided marketplaces taking into account the uniqueness of every job and candidate, boosting network effects and steering on high liquidity
• You have an interest in person-job matching and the future of work
• You have excellent English verbal and written communication skills
• You have expert data science coding skills in Python and are strong in SQL
• You have experience working within data warehouses, production codebases, AI-powered tools or similar
• You have strong problem-solving skills to navigate the complexities of AI and talent analytics
• You have strong organizational skills to keep work on track and to a high standard
• You are motivated by new challenges, breaking new ground, dealing with complexity, and you are willing to fail or try non-traditional approaches
• You are comfortable collaborating with a team spread across the globe and in different time zones

Desirable

• The Lead Data Scientist is a newly created role in the Talent Marketplace Data Science squad
• You will contribute to our hiring philosophy and help shape the future of work and skills-based hiring
• You'll play a pivotal role in shaping how we leverage data and technology to understand and assess skills for optimal talent matching and decision-making
• You will bring innovation, creativity, and subject matter expertise in data science and analytics to understand people and jobs, and develop a recommender system that connects people and jobs
• You will also support our expansion into related domains by exploring new methods and innovations in talent matching, evaluation tools, test development, and additional ways to generate and capitalize on data-driven insights and analytics
• This role focuses on experimenting and developing AI, ML, and related methods to data associated with psychometrics and user behaviors
• The primary project will be applying this to develop recommender systems and matching algorithms for the Talent Marketplace and deliver pragmatic, commercial solutions that evolve our product
• This role is expected to work closely with other scientists, data engineers, and software developers, often partnering to solve problems and work on initiatives along with other science and product development team members
• As a lead of the data science team, you collaborate with other senior technical leaders and are recognized as a decision-maker within your area of expertise
• Tackling key challenges in talent and job data science by contributing to:
• ML algorithms, particularly in the context of recommender systems (e.g., content-based filtering, collaborative filtering, SVD++)
• Mitigating bias and explainability:
• Supporting initiatives to de-bias algorithms and provide interpretable explanations for insights
• Developing explainable models and avoiding “black box” algorithms
• Promoting inclusion, fairness, and transparency in talent assessment
• Advising on relevant job and skill taxonomy activities, including models of job success and performance
• Supporting innovation and enhancing talent assessment:
• Identifying and deploying data-driven solutions:
• Leveraging AI/ML, new technologies, and research to improve our competitive edge
• Collaborating with interdisciplinary teams to analyze problems and offer solutions
• Validating data-driven decision making:
• Performing in-depth data analysis to identify trends, patterns, and insights that drive actionable recommendations and communicate certainty of findings that inform better talent decisions
• Navigating the evolving landscape:
• Staying informed about evolving legal regulations of AI hiring tools
• Contributing to discussions on platform liability and ethical use
• Leveraging domain expertise:
• Collaborating with subject matter experts to interpret skill data accurately
• Providing actionable insights for customers
• Sharing insights and best practices through internal and external thought leadership