Principal Engineer - Data Science

Company:

MMC Corporate

Description:

We are seeking a talented individual to join our CIS team at Marsh McLennan. This role will be based in Pune/Mumbai. This is a hybrid role that has a requirement of working at least three days a week in the office.

Principal Engineer - Data Science

We will count on you to:

• Design and implement machine learning pipelines that curate datasets, train, test, and validate models, and deploy them into production.
• Utilize LLMs and Generative AI techniques to enhance data analysis, natural language processing, and predictive modeling capabilities.
• Apply supervised, unsupervised, and deep learning techniques to address business challenges, including the development of recommendation engines and scoring systems.
• Conduct A/B testing and utilize statistical techniques to evaluate model performance and improve outcomes.
• Collaborate with cross-functional teams to integrate machine learning solutions into existing systems and processes.
• Proficient in model building using R or Python, with a strong focus on data mining and leveraging large datasets.
• Demonstrated ability to design and build machine learning pipelines, including model training, validation, and deployment.
• Deep understanding of statistical and machine learning modeling, with experience applying these techniques to real-world business problems.

What You Need To Have:

• Bachelor’s or Master’s degree in Computer Science, Data Science
• Minimum of 3+ years of experience in data science
• Hands-on experience with analytics and big data technologies, particularly within Microsoft Azure, including Azure Machine Learning, Azure Cognitive Services, and Azure Databricks.
• Working knowledge of machine learning frameworks and tools such as scikit-learn, TensorFlow, PyTorch, and ONNX.
• Experience with LLMs and Generative AI frameworks, including fine-tuning and deploying models for specific applications.
• Experience with cloud service machine learning platforms, preferably Azure, and setting up data and experimental platforms.
• Extensive knowledge of model management, ideally using Azure ML service and/or ML Flow, as well as deployment using Azure Kubernetes Service.

What Makes You Stand Out:

• Excellent verbal and written communication skills, comfortable interfacing with business users
• Good troubleshooting and technical skills
• Able to work independently

Why Join Our Team:

• We help you be your best through professional development opportunities, interesting work and supportive leaders.
• We foster a vibrant and inclusive culture where you can work with talented colleagues to create new solutions and have impact for colleagues, clients and communities.
• Our scale enables us to provide a range of career opportunities, as well as benefits and rewards to enhance your well-being.

Marsh McLennan (NYSE: MMC) is a global leader in risk, strategy and people, advising clients in 130 countries across four businesses: Marsh, Guy Carpenter, Mercer and Oliver Wyman. With annual revenue of $24 billion and more than 90,000 colleagues, Marsh McLennan helps build the confidence to thrive through the power of perspective. For more information, visit marshmclennan.com, or follow on LinkedIn and X.Marsh McLennan is committed to embracing a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age, background, caste, disability, ethnic origin, family duties, gender orientation or expression, gender reassignment, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, or any other characteristic protected by applicable law.Marsh McLennan is committed to hybrid work, which includes the flexibility of working remotely and the collaboration, connections and professional development benefits of working together in the office. All Marsh McLennan colleagues are expected to be in their local office or working onsite with clients at least three days per week. Office-based teams will identify at least one “anchor day” per week on which their full team will be together in person.