Senior Data Scientist

Requirements

• Knowledge of professional software engineering practices & standard methodologies for the full software development process, including coding standards, code reviews, source control management, build processes, testing, and operations.
• Strong collaboration and elaboration skills; demonstrates a strong commitment to organizational success; shares resources and demonstrates knowledge across the organization.
• Strong problem-solving skills and the ability to think critically and creatively to develop innovative solutions.
• Excellent communication and collaboration skills, with the ability to work optimally in multi-functional teams.
• Strong problem solving mindset with the ability to think critically and creatively.
• Excellent communication and collaboration skills, with comfort working in cross functional teams.
• 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.
• #LI-Hybrid
• The role being advertised is an existing vacancy.
• About Manulife and John Hancock
• Manulife Financial Corporation is a leading international financial services provider, helping people make their decisions easier and lives better. To learn more about us, visit https://www.manulife.com/en/about/our-story.html .
• Manulife is an Equal Opportunity Employer

Benefits

Additional Information

We are seeking a highly skilled and motivated Senior Data Scientist - GenAI to join the Long Term Care (LTC) Fraud, Waste & Abuse (FWA) Advanced Analytics team. In this role, you will lead the design and deployment of high impact machine learning and AI solutions-combining traditional statistical modeling with advanced AI and Generative AI techniques including but not limited to generative AI techniques, such as applying prompt engineering, working with RAG applications, fine-tuning LLM models, and deploying applications on cloud platforms like Azure. You will work closely with business partners to translate complex fraud and risk problems into scalable, production ready analytical and AI solutions. Your work will directly protect policyholders, ensure program integrity, and improve operational efficiency. Position Responsibilities: Lead end to end development of fraud detection and risk analytics models, including probabilistic models, anomaly detection, and ensemble approaches. Perform advanced modeling and AI analysis on large, complex datasets to identify emerging fraud patterns and root causes. Demonstrate proficiency in developing, fine-tuning, and deploying Generative AI models such as GPT models, as well as other innovative architectures. This includes understanding the nuances of model selection, optimization, and evaluation to ensure high performance and accuracy, while managing cloud-based solutions to ensure scalability, security, and performance. Build and optimize modular RAG (Retrieval-Augmented Generation) systems, apply advanced retrieval methods, and evaluate and monitor the performance of RAG systems. Apply large language models (LLMs) to parse complex documents and handle diverse layouts, such as multi-column text, tables, and images, and converting them into machine-readable formats to extract structured information. Stay up-to-date with the latest advancements in Generative AI, prompt engineering, Agentic Framework, and cloud technologies, to apply them and enhance our data science capabilities. Collaborate with data engineers and ML engineers to integrate data science solutions into existing systems and workflows. Communicate sophisticated technical concepts and findings to both technical and non-technical partners, ensuring clear understanding and agreement. Review the work of other Data Scientists and take part in model and code reviews. Collaborate with multi-functional teams to identify and define business requirements, ensuring alignment with data science objectives. Required Qualifications: Master's, or Ph.D. degree in Computer Science, Data Science, Statistics, Engineering, Physics, or a related quantitative field. 5+ years of hands on industry experience in developing probabilistic models, analytics, and machine learning algorithms, including real world experience of applying analytics models, with a strong focus on machine learning, generative AI, timely engineering, and RAG applications Proficiency in programming languages such as Python and experience with machine learning libraries/frameworks, e.g., PyTorch, scikit-learn, Hugging Face, SQL, graph databases (Neo4j/Cypher, Cosmos DB

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