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Data ScientistManulife • United States
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Data Scientist

Manulife
  • US
    United States
  • US
    United States
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About

Data Scientist
John Hancock's AI team works across the US Insurance business to optimize our sales and marketing activities, make life insurance easy to buy, and streamline in-force and claims operations! We have close relationships with functional leadership across the business and a strong working relationship with technology partners. As part of a 160+ member community of data scientists, John Hancock, and our parent Manulife, you will have an opportunity to network with experts across the globe. John Hancock is a unit of Manulife Financial Corporation, a leading international financial services group offering insurance and wealth management solutions in the US, Canada, Europe, and Asia! We are on a transformational journey. We want to remove complexity from the financial services industry, to make people's lives better and decisions easier. Being part of this transformation is hugely exciting and offers talented, ambitious people an amazing opportunity to build a career. We are seeking a skilled Data Scientist with a strong focus on developing models leveraging conventional AI and GenAI techniques. The ideal candidate will have experience in designing and executing data science experiments, developing scalable and robust machine learning models, implementing data pipelines, and deploying AI solutions in production. Position Responsibilities: Building of GenAI and AI solutions, including but not limited to analytical model development and implementation, prompt engineering, general all-purpose programming (e.g., Python), testing, communication of results, front end and back-end integration, and iterative development with clients. Collaborating with business teams to understand their business problems, gather requirements, create initial hypothesis, and development and deployment of GenAI and AI solution approach. Designing and solutioning AI/GenAI architectures (LLM/SLM/Machine learning models) to address real business problems. Leading and contributing to development of proof of concepts, pilots, and production use cases for business teams while working with cross-functional teams. Generate useful insights based on iterative data analysis and make appropriate recommendations to business partners. Continuously monitor and improve the performance of AI solutions through data analysis and testing. Strong communication to help business partners better understand the use of data, ML models, and AI solutions. Stay up-to-date with the latest trends and advancements in cloud-based machine learning platforms, technologies, and tools. Required Qualifications: At least 2-year of industry experience in developing and deploying models using AI and GenAI techniques. Proficient in programming using Python and SQL. Master's degree or PhD degree in quantitative fields such as Statistics, Applied Mathematics, Data Science, Engineering, or Computer Science or Physics. Preferred Qualifications: Experience in using Python (e.g., Pandas, NLTK, Scikit-learn, Keras etc.), common LLM development frameworks (e.g., Langchain, Semantic Kernel), Relational storage (SQL), Non-relational storage (NoSQL). Hands-on experience in developing models solving NLP tasks, including Document Classification, Entity Extraction, Entity Relation Extraction, Sentiment Analysis, etc. Experiences in fraud prevention and deep-fake detection. Strong understanding of medical terminology, EHR data, and coding systems. Proven problem-solving abilities, including conducting root cause analysis to address specific business inquiries and identify opportunities for enhancement. Excellent communication skills to explain complex topics to diverse audiences. Demonstrated expertise in the data analytics life cycle, encompassing problem framing, data collection, data cleansing, insights generation, reporting, and communication. Skilled in machine learning modeling life cycle, including exploratory data analysis, data cleansing, feature engineering, model building, deployment, and monitoring. Good understanding of vectorization and embedding, prompt engineering, RAG, Multi-agent techniques. Solid knowledge and highly skilled in supervised and unsupervised machine learning algorithms, deep learning, and LLMs. Experience in developing and deploying models in cloud-based environments, specifically Microsoft Azure, and Databricks, following MLOps best practices. Experience with Git Version Control, Unit/Integration/End-to-End Testing, CI/CD, release management, etc. When you join our team: We'll empower you to learn and grow the career you want. We'll recognize and support you in a flexible environment where well-being and inclusion are more than just words. As part of our global team, we'll support you in shaping the future you want to see.
  • United States

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