Data Scientist

At Manulife Business Processing Services, a Data Scientist is responsible for collaborating with multiple teams to deliver data-driven insights, develop scalable data solutions, and support various business objectives. This includes identifying opportunities for data initiatives, researching and prototyping Machine Learning and GenAI solutions, and evaluating new technologies and tools to enhance team capabilities.

To apply for the Data Scientist position at Manulife, candidates should possess a degree in Statistics, Mathematics, Computer Science, Engineering, or a related technical field. Additionally, a minimum of 5 years of applicable experience in programming languages such as R and Python, as well as familiarity with Machine Learning algorithms, is required for the role.

Essential skills for a Data Scientist at Manulife include advanced programming knowledge in languages like R and Python, proficiency in querying both structured and unstructured data, and strong communication abilities to convey complex ideas clearly. Experience with AI solutions and cloud platforms like Azure or AWS would greatly benefit applicants.

Manulife is dedicated to the career development of its Data Scientists through extensive training resources, a collaborative culture, and opportunities to work on innovative projects. The supportive environment and leadership encourage continuous learning and professional growth.

The Data Scientist role at Manulife offers a hybrid working arrangement, allowing employees the flexibility to balance between working remotely and being in the office.

A great answer should detail the project’s objective, your role within the team, the methodology you used, and the outcomes. Highlight your specific contributions, any challenges faced, and how you utilized data to drive decision-making.

Be prepared to discuss algorithms like linear regression, decision trees, and neural networks. Explain how you've applied these algorithms in past projects and the context in which they were used, emphasizing results achieved.

Discuss techniques for processing unstructured data such as text or images. Mention tools you've used and how you transformed that data into structured formats to derive insights effectively.

You might mention tools like Power BI, Plotly, or R Shiny. Discuss your approach to selecting appropriate visualization techniques to represent findings and facilitate data communication.

Discuss data validation techniques, the importance of clean data, and the steps you take to verify your analyses, including any statistical tests used to check assumptions.

Talk about your experience with platforms like AWS, Azure, or GCP. Provide examples of how you utilized these platforms in your data science projects for storage, processing, or deploying models.

Share strategies for simplification, such as using analogies, visual aids, and focusing on the implications of the data rather than technical jargon. Mention the importance of storytelling in data presentations.

If applicable, describe any projects where you developed APIs for data interaction or created front-end applications, detailing the technologies used and the purpose of these developments.

Explain your approach to continued learning, such as following industry blogs, participating in online courses, or attending workshops and conferences. Mention any specific areas you're particularly passionate about.

Share specific projects where you have implemented natural language processing or artificial intelligence techniques, noting challenges you faced and how you overcame them to achieve your project goals.