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About This Role
The Opportunity
We are seeking a Lead Data Scientist to develop advanced analytics and AI solutions that improve underwriting decisions, expand automation, and deliver measurable business value.
The successful candidate will lead complex analytical work from problem definition through model development, implementation, and ongoing enhancement. This role requires strong data science expertise, solid life insurance underwriting knowledge, and the ability to provide technical direction across projects.
Job Summary
The Lead Data Scientist is responsible for developing and delivering statistical and machine-learning solutions for automated underwriting and related business problems. The role translates complex business needs into rigorous analytical approaches and ensures that solutions are accurate, interpretable, scalable, and practical for business use.
This is a hands-on role that also provides technical direction, reviews analytical work, mentors other data scientists, and communicates recommendations to technical and business stakeholders.
Position Responsibilities
- Partner with underwriting, business, product, and technology stakeholders to identify and prioritize data science opportunities.
- Lead data science projects from problem definition and data exploration through modeling, evaluation, implementation, and enhancement.
- Develop statistical and machine-learning models using techniques appropriate to the business problem, available data, and intended use.
- Analyze large and complex datasets to identify patterns, generate insights, and support business decisions.
- Define model-evaluation approaches and ensure solutions are accurate, interpretable, stable, and aligned with business objectives.
- Evaluate new data sources, analytical methods, and third-party solutions through structured analysis and experimentation.
- Collaborate with data engineers, machine-learning engineers, software engineers, and technology teams to implement analytical solutions in production.
- Monitor model performance, investigate unexpected outcomes, and recommend improvements as data and business conditions evolve.
- Prepare clear technical documentation to support implementation, validation, governance, and ongoing model management.
Provide technical direction, review analytical work, and mentor data scientists and other analytical contributors. *
Qualifications
- Bachelor’s or advanced degree in Statistics, Mathematics, Data Science, Computer Science, Engineering, Actuarial Science, Economics, or a related quantitative field.
- 8 years of experience applying statistics, machine learning, or predictive analytics to complex business problems.
- Strong knowledge of statistical modeling, machine learning, model evaluation, and experimental design.
- Proficiency in Python or R and SQL, with experience working with large and complex datasets.
- Demonstrated experience developing analytical solutions from concept through production implementation and monitoring.
- Experience leading analytical workstreams and providing technical guidance to other data scientists.
- Strong problem-solving, communication, and technical documentation skills.
- Solid experience in life insurance underwriting, automated underwriting, risk selection, or related insurance analytics.
- Experience with cloud-based analytics platforms, model governance, external data evaluation, or third-party model assessment is an asset.
Familiarity with actuarial concepts, mortality analytics, or an actuarial designation is an asset but not required. *
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.
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
At Manulife/John Hancock, we embrace our diversity. We strive to attract, develop and retain a workforce that is as diverse as the customers we serve and to foster an inclusive work environment that embraces the strength of cultures and individuals. We are committed to fair recruitment, retention, advancement and compensation, and we administer all of our practices and programs without discrimination on the basis of race, ancestry, place of origin, colour, ethnic origin, citizenship, religion or religious beliefs, creed, sex (including pregnancy and pregnancy-related conditions), sexual orientation, genetic characteristics, veteran status, gender identity, gender expression, age, marital status, family status, disability, or any other ground protected by applicable law.
It is our priority to remove barriers to provide equal access to employment. A Human Resources representative will work with applicants who request a reasonable accommodation during the application process. All information shared during the accommodation request process will be stored and used in a manner that is consistent with applicable laws and Manulife/John Hancock policies. To request a reasonable accommodation in the application process, contact [email protected] .
Referenced Salary Location
Boston, Massachusetts Working Arrangement
Hybrid Salary range is expected to be between
$127,330.00 USD - $236,470.00 USD Employees also have the opportunity to participate in incentive programs and earn incentive compensation tied to business and individual performance. The actual salary will vary depending on local market conditions, geography and relevant job-related factors such as knowledge, skills, qualifications, experience, and education/training. If you are applying for this role outside of the primary location, please contact [email protected] for the salary range for your location.
Manulife/John Hancock offers eligible employees a wide array of customizable benefits, including health, dental, mental health, vision, short- and long-term disability, life and AD&D insurance coverage, adoption/surrogacy and wellness benefits, and employee/family assistance plans. We also offer eligible employees various retirement savings plans (including pension/401(k) savings plans and a global share ownership plan with employer matching contributions) and financial education and counseling resources. Our generous paid time off program in the U.S. includes up to 11 paid holidays, 3 personal days, 150 hours of vacation, and 40 hours of sick time (or more where required by law) each year, and we offer the full range of statutory leaves of absence.
We use data and analytics technologies, such as artificial intelligence (AI), and automated processing tools, to analyze and process the information you provide to us or third parties in the application process. For more information, please refer to our personal information collection statement .
Know Your Rights I Family & Medical Leave I Employee Polygraph Protection I Right to Work I E-Verify
Company: John Hancock Life Insurance Company (U.S.A.)
Salary Context
This $127K-$236K range is above the median for Data Scientist roles in our dataset (median: $161K across 138 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Manulife, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $127K to $236K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Manulife AI Hiring
Manulife has 10 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Boston, MA, US, Toronto, Ontario, Canada, Hong Kong, Hong Kong SAR. Compensation range: $236K - $236K.
Location Context
AI roles in Boston pay a median of $215,000 across 360 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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