Preparation should focus on demonstrating both your technical toolkit and your ability to thrive in a collaborative, business-oriented environment.

Technical Competency – You must be fluent in the core languages and tools used at John Hancock, primarily SQL and standard data manipulation libraries. Expect to demonstrate your ability to write clean, efficient code that solves real-world data problems.

Business Alignment – We evaluate your ability to link your work to the broader goals of the insurance and finance industry. Strong candidates show a clear understanding of how their analytical output directly impacts the bottom line or customer experience.

Communication and Collaboration – You will frequently present to non-technical stakeholders. Your ability to distill complex findings into clear, concise, and actionable recommendations is as critical as your ability to build the model itself.

The interview process at John Hancock is designed to evaluate your technical foundation, your ability to handle ambiguity, and your fit within our organizational culture. You can expect a multi-stage process that begins with a recruiter screen, followed by a series of technical and behavioral assessments with hiring managers and senior leaders.

The progression is intentional and rigorous, moving from verifying your core skills to assessing your potential for long-term growth within the company. We value candidates who show curiosity, a structured approach to problem-solving, and a genuine interest in the impact of their work on our clients' financial futures.

The visual timeline above illustrates the typical progression from initial screening to final-round interviews with senior leadership. Candidates should interpret these stages as an opportunity to build a narrative; each round is a chance to deepen the interviewer's understanding of your expertise and your ability to navigate our corporate environment. Plan your preparation to ensure you are comfortable discussing both the "how" (technical) and the "why" (business strategy) behind your previous projects.

SQL and Data Manipulation

This is the bedrock of our analytical work. We look for proficiency in querying, transforming, and validating data.

Be ready to go over:

• Advanced SQL queries – Using complex joins, window functions, and subqueries to extract specific insights.
• Data Integrity – How you validate the accuracy of your results before presenting them.
• Query Optimization – Techniques for improving the speed and scalability of your code.

Example scenarios:

• "How would you handle a situation where two sources of data provide conflicting information for the same customer?"
• "Explain your approach to troubleshooting a query that is returning unexpected results."

Business Problem Solving

We need to know that you can translate a vague business requirement into a structured data project.

Be ready to go over:

• Requirement Gathering – How you clarify ambiguous requests from stakeholders.
• Prioritization – How you decide which data projects provide the most value.
• Impact Measurement – How you define success for a data-driven initiative.

Example scenarios:

• "A director asks for a model to predict churn; what is the first thing you ask them?"
• "Describe a time you had to pivot your approach because the initial data didn't support the business hypothesis."

As a Data Scientist, your day-to-day will involve identifying patterns in massive datasets to support our business units. You will be responsible for the full lifecycle of data projects, from initial data extraction and cleaning to the development of models that inform risk management or customer engagement strategies.

Collaboration is central to your role. You will work closely with stakeholders to understand their pain points and translate those into technical requirements. This requires you to be comfortable in both a terminal or IDE and a meeting room, effectively communicating the limitations and strengths of your models to those who may not have a technical background.

Successful candidates for this role typically possess a blend of technical expertise and a pragmatic, business-focused mindset.

• Must-have skills:

• Strong proficiency in SQL for complex data extraction and transformation.

• Experience with Python or R for data analysis and modeling.

• Excellent verbal and written communication skills for stakeholder engagement.

• Proven ability to manage multiple projects concurrently.

• Nice-to-have skills:

• Experience in the Financial Services or Insurance sector.

• Familiarity with cloud-based data environments.

• Experience with data visualization tools to present findings.

Q: How long does the interview process typically take? The process often spans several weeks, involving multiple rounds of interviews. While we aim for efficiency, the timeline can vary; it is best to maintain consistent communication with your recruiter.

Q: Are the technical questions very difficult? The technical component is focused on practical application rather than obscure algorithms. If you have a solid grasp of SQL and data manipulation, you will be well-prepared.

Q: What is the most important trait for a candidate to show? Beyond technical skills, we value the ability to communicate the "so what" of your analysis. Show us that you understand how your work drives the business forward.

Q: Is the role fully remote? Please confirm the specific work arrangements for your target team with your recruiter, as policies regarding office presence can vary by department and location.

• Structure your answers: Use the STAR method (Situation, Task, Action, Result) to ensure your behavioral answers are concise and impactful.
• Be ready to explain your resume: Every project you list is fair game. Know the "why" behind every tool and methodology you chose to use.
• Focus on the business: Always tie your technical answers back to how they solve a problem or create value for John Hancock.
• Ask meaningful questions: At the end of your interviews, ask about the team's current challenges or how they measure success. This shows you are already thinking like a member of the team.

A Data Scientist role at John Hancock offers a unique opportunity to apply advanced analytical techniques within a stable, high-impact industry. By focusing your preparation on SQL proficiency, business-oriented problem solving, and clear communication, you will be well-positioned to succeed in our interview process.

Remember that we are looking for teammates who can bridge the gap between complex data and strategic business decisions. Review your past projects, prepare your technical fundamentals, and approach each interview with confidence. For further insights and to track your preparation progress, continue utilizing the resources available on Dataford. You have the skills to make a significant impact here; we look forward to seeing how you apply them.

The provided salary data offers a benchmark for compensation expectations in the industry. Use this to ensure your expectations align with the market and the level of the role for which you are interviewing. Keep in mind that total compensation at John Hancock may also include benefits and performance-based incentives not fully captured in raw salary figures.