Data Scientist II

You will collaborate with other data scientists, data/ML engineers, designers, project managers, and business subject matter experts on interdisciplinary projects in various industry sectors to support business objectives using data & analytics. Your role requires a high level of collaboration, willingness to listen and learn from colleagues, and the ability to challenge thoughtfully while focusing on impact. You should continuously seek ways to enhance processes and work closely with your team members to drive iterative change and improvement. While emphasizing the importance of using the appropriate tools for each task, you will also consider clients" current environments and preferences. Commonly used tools include Python, PySpark, TensorFlow, PyTorch, Databricks, SQL, Docker, and Kubernetes. Additionally, proprietary tools like Kedro, CuasalNex, and MLRun are utilized. Collaboration with cloud service providers such as AWS, GCP, and Azure is a regular practice. As a Data Scientist, your responsibilities will include: - Solving complex business challenges for clients globally through advanced Machine Learning and statistical methods - Leading the research and development of cutting-edge AI and deep learning solutions - Identifying high-potential machine learning R&D initiatives applicable to various industries - Collaborating with QuantumBlack leadership and clients to align business problems with analytics and AI solutions - Working with a diverse team to develop end-to-end analytics solutions that drive tangible impact for clients - Contributing to one of the most skilled and diverse data science teams in the industry Your impact at McKinsey will involve working on impactful projects across diverse industries, collaborating with QB/Labs teams, and developing innovative ML systems to address business challenges efficiently. You will have the opportunity to grow as a technologist and leader by working on real-world problems and engaging with industry experts to deliver value to clients effectively. Your qualifications and skills should include: - A bachelor's or master's degree in computer science, machine learning, applied statistics, mathematics, engineering, or artificial intelligence - 2+ years of technical experience in machine learning, advanced analytics, and statistics - Proficiency in Python programming - Demonstrated application of advanced analytical and statistical methods in practical scenarios - Strong presentation and communication skills to convey complex concepts to technical and non-technical audiences - Knowledge of engineering standards and QA/risk management - Experience in GenAI application development and API integration using tools like Langchains, Crew.AI, and AutoGen is advantageous - Readiness to travel domestically and internationally for project requirements,