Senior Data Scientist

Description

Job Summary: Senior Data Scientist will lead the complete modeling lifecycle—from ideation to deployment—leveraging AI to solve complex business problems and impact millions. Key Highlights: 1. Lead the full lifecycle of data modeling and artificial intelligence. 2. Optimize strategic decision-making with advanced data products. 3. Impact millions of users and enterprises with innovative solutions. **About the Challenge** Your mission is to build advanced data products to optimize strategic decision-making on the world’s largest reputation platform. As a Senior Data Scientist, you’ll have autonomy to lead the end-to-end modeling lifecycle—from ideation to production deployment—using artificial intelligence to solve complex business problems and impact millions of users and enterprises. **Responsibilities and Duties** * **End-to-End Modeling:** Develop, train, validate, and deploy machine learning models (classification, regression, recommendation, NLP) to optimize processes and products. * **Strategic Analysis:** Conduct rigorous statistical studies and exploratory analyses on large-scale datasets to inform C-level decisions. * **Stakeholder Collaboration:** Lead discussions with Product, Engineering, and Marketing teams to translate business challenges into robust algorithmic solutions. * **Quality and Rigor:** Implement rigorous testing and validation (A/B tests, drift monitoring) to ensure model reliability and impact. * **Technical Culture:** Promote MLOps best practices and stay current with industry trends to propose innovations. **Requirements (What We’re Looking For)** * **Senior Experience:** Proven track record developing and deploying ML models in real production environments (handling “dirty” data and scalability challenges). * **Python Proficiency:** Expertise in the scientific stack (Pandas, NumPy, Scikit-Learn, XGBoost/LightGBM). * **Strong Theoretical Foundation:** In-depth knowledge of Statistics, Probability, Linear Algebra, and Learning Theory. * **Executive Communication:** Ability to clearly communicate technical points, decisions, and *trade-offs* to non-technical audiences. * **Business Acumen:** Capacity to align analytical outcomes with company priorities. **Nice-to-Haves** * Experience with deep learning libraries (TensorFlow, PyTorch). * Familiarity with causal inference concepts applied to business. * Experience with MLOps tools and cloud platforms (GCP preferred).