Lead Data Scientist

Your responsibilities will include

• Working alongside Oliver Wyman consulting teams and partners, engaging directly with global clients to understand their business challenges
• Exploring large-scale data and crafting models to answer core business problems
• Working with partners and principals to shape proposals that showcase our data science and analytics capabilities
• Explaining, refining, and crafting model insights and architecture to guide stakeholders through the journey of model building
• Advocating best practices in modelling and code hygiene
• Leading the development of proprietary statistical techniques, ML algorithms, assets, and analytical tools on varied projects
• Travelling to clients locations across the globe, when required, understanding their problems, and delivering appropriate solutions in collaboration with them
• Keeping up with emerging state-of-the-art modelling and data science techniques in your domain

Your Attributes, Experience & Qualifications

• Bachelors or Master s degree in a quantitative discipline from a top academic program (Data Science, Mathematics, Statistics, Computer Science, Informatics, and Engineering)
• Prior experience in data science, machine learning, and analytics
• Passion for problem-solving through big-data and analytics
• Pragmatic and methodical approach to solutions and delivery with a focus on impact
• Independent worker with the ability to manage workload and meet deadlines in a fast-paced environment
• Impactful presentation skills that succinctly and efficiently convey findings, results, strategic insights, and implications
• Excellent verbal and written communication skills and complete command of English
• Willingness to travel
• Collaborative team player
• Respect for confidentiality

Technical Background

• Proficiency in modern programming languages (Python is mandatory; SQL, R, SAS desired) and machine learning frameworks (e.g., Scikit-Learn, TensorFlow, Keras/Theano, Torch, Caffe, MxNet)
• Prior experience in designing and deploying large-scale technical solutions leveraging analytics
• Solid foundational knowledge of the mathematical and statistical principles of data science
• Familiarity with cloud storage, handling big data, and computational frameworks
• Valued but not required:
• Compelling side projects or contributions to the Open-Source community
• Experience presenting at data science conferences and connections within the data science community