Lead Data Scientist - Hybrid

Manulife•Waterloo, ON

2d•$127,330 - $236,470•Hybrid

About The Position

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. 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.

Requirements

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.

Nice To Haves

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.

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.