Senior Data Scientist - GenAI

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.
• 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
• It is our priority to remove barriers to provide equal access to employment. A Human Resources represen

Benefits

Additional Information

We are seeking a highly skilled and motivated Senior Data Scientist to join our Group Functions Advanced Analytics team. As a Senior Data Scientist - GenAI, you will develop and implement robust analytical solutions for various parts of Manulife and JH by applying traditional and emerging ML and 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. Your expertise in these areas will play a crucial role in driving data-driven decision-making and enhancing our business processes. Position Responsibilities: Develop and lead machine learning and AI driven projects that span the breadth of Group Functions (Corporate Functions) with high business impact Apply generative AI techniques to generate synthetic data, create realistic simulations, and enhance data analysis capabilities. Apply prompt engineering techniques, build with RAG (Retrieval-Augmented Generation) applications, and fine-tune language models and improve their performance in specific tasks. Design and complete experiments to validate and optimize machine learning models, ensuring accuracy, efficiency, and scalability. Deploy machine learning models and applications on cloud platforms like Azure ML or Databricks, ensuring flawless integration and scalability. Stay up-to-date with the latest advancements in machine learning, generative AI, prompt engineering, RAG applications, and cloud technologies, and apply them to 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 Peer review other Data Scientists' work and participate in model and code reviews Clean, preprocess, and analyze large datasets to extract meaningful insights and patterns. Collaborate with multi-functional teams to identify and define business requirements, ensuring alignment with data science objectives. Required Qualifications: Bachelor', Master's degree, or Ph.D. in Computer Science, Data Science, Statistics, Engineering, or a related field. 5+ years of experience in developing probabilistic models, data mining, and machine learning algorithms, including real world experience of applying analytics models, with a strong focus on machine learning, generative AI, prompt 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/Gremlin).

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