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McKinsey & Company Inc. US is seeking a Senior Data Scientist. Structure ambiguous business problems, form hypothesis, and synthesize clear takeaways from complex information into clear takeaways and recommendations (e.g., in PPT pages). Gather and advise clients on industry best practices related to data management (e.g., data quality, data lineage, data operating models, data tracing, data transformation strategies). Conduct research to establish industry-wide benchmarks (e.g., related to competitors, 3rd party vendors, climate strategies, decarbonization targets) to inform business decisions in client situations. Translate analytical results (e.g., prediction outputs) obtained using quantitative methods into business insights and actionable recommendations. Build python-based data loading, preprocessing, and feature engineering pipelines at-scale, using solutions catered to big data (e.g., Spark). Build visualization dashboards (e.g., using python, tableau, excel) to visualize analytical results, model features, targets, and explainability. Design and build generative AI workflow and models (e.g., chatbots, document QA) on selected business use cases having large amounts of textual data, working with the modern generative AI tech stack (e.g., langchain, vector databases, OpenAI models). Build python-based end-to-end AI/ML pipelines (e.g., using kedro) focusing on supervised (e.g., classification), unsupervised (e.g., clustering), and self-supervised (e.g., language model pre-training) methods, to be deployed in various client situations (e.g., customer channel preference, churn predictions, customer segmentation). Build epidemiology models to inform healthcare-related decision making by enterprises and national governments. Design, implement, and deploy data quality solutions for structured data (e.g., tabular data in databases) and unstructured data (e.g., textual information in collections of PDFs) for detection, recommendation, and correction of data quality issues (e.g., completeness, accuracy, timeliness, suitability for GenAI applications). Work with clientfacing colleagues to deploy, maintain, and monitor AI/ML solutions on the cloud (e.g., aws, Azure), including performing Machine Learning Operations (MLOps) related activities (e.g., CI/CD, data drift monitoring). Conduct research on AI/ML related fields (e.g., Natural Language Processing) to codify into firm assets, share with internal colleagues, and potentially deploy at client situations. Apprentice and coach junior colleagues.Qualified applicants for this position must possess a Master’s degree in Data Science, Computational Science, or related field, or foreign degree equivalent. Qualified applicants for this position must have a minimum one (1) year of experience developing advanced analytics and data science solutions. Experience must include the following: statistical knowledge including advanced machine learning techniques and optimization; building production-ready machine learning models; knowledge of cloud architecture and software development; using database management tools: SQL; using visualization tools: Plotly, QlikView, Tableau; coding skills in Python and SQL; data engineering and processing including advanced and memory-optimized data processing using Spark and Pandas; and data quality assessment and mitigation; knowledge to build reusable modules for data engineering and data science; basic knowledge of the generative AI tech stack. Salary $175,500 - $189,000. Email your resume to CO@mckinsey.com & refer to Job # 7161306.Worksite: McKinsey & Company Inc. US, 711 Third Ave, New York, New York 10017 (HQ). 100% remote; can work from company HQ or from a home office anywhere in the U.S.

Minimum Salary: 175500 Maximum Salary: 189000 Salary Unit: Yearly