Your Opportunity:
We have an exciting opportunity for a Senior Epidemiologist to help shape the future of cancer care in Alberta through advanced analytics and evidence-informed decision-making. At Cancer Care Alberta, our goal is to be a leader in cancer diagnosis, treatment, survivorship, and palliative care, supported by innovative analytics that improve patient outcomes, system performance, and access to care. Situated within Cancer Research & Business Partnerships, Precision Analytics is a multidisciplinary team of epidemiologists, biostatisticians, machine learning engineers, and implementation scientists dedicated to solving complex healthcare challenges through data.
We partner closely with clinical and operational teams to develop analytical solutions that support planning and decision-making across the cancer care system. As a Senior Epidemiologist, you will play a central role in delivering high-priority analytics initiatives that address complex clinical, operational, and health system questions. Using large real-world administrative and clinical datasets, you will apply advanced epidemiologic, biostatistical, and health services research methods to generate actionable evidence that informs cancer system planning, service delivery and quality improvement.
This is a highly applied, hands-on role with a strong focus on developing analytical code, data pipelines, statistical models, and reproducible workflows using Python. You will translate healthcare questions into rigorous analytical solutions and contribute epidemiologic expertise to advanced analytics and machine learning initiatives. Success in this role requires strong problem-solving and independent learning skills, with the ability to evaluate and apply emerging methods and technologies to complex healthcare challenges.
This is a one-year term position with the possibility of extension and a hybrid work arrangement.
Description:
As a Senior Epidemiologist at Cancer Care Alberta, you will be a key scientific member of the Precision Analytics team, working under the guidance of the Program Lead to support high-impact analytics initiatives across the cancer care continuum. You will apply advanced expertise in epidemiology, biostatistics, health services research, applied health analytics, and Python-based programming to solve complex clinical, operational, and health system challenges.
Working with a high degree of independence, you will collaborate with epidemiologists, biostatisticians, machine learning engineers, and clinical and operational experts to translate healthcare questions into rigorous analytical solutions. You will lead and conduct analyses using large real-world administrative and clinical datasets, including data preparation, statistical modelling, interpretation of results, and development of reproducible analytic workflows. Your work will generate evidence to support cancer system planning, service delivery, quality improvement, and operational decision-making.
You will apply advanced epidemiologic and health services research methods, including observational study design, causal inference, and program evaluation, to generate actionable evidence. You will develop reusable analytical code, data pipelines, and statistical models, while providing epidemiologic expertise to machine learning initiatives through. Success in this role requires strong problem-solving and independent learning skills, with the ability to critically evaluate, adapt, and apply emerging methodologies and technologies to complex analytical challenges.
The Senior Epidemiologist will also contribute to team growth through mentorship, promotion of best practices, and advancement of analytical capabilities across the team.
Additional
Required Qualifications:
Strong project management and organizational skills, with the ability to manage multiple priorities and deliver high-quality work independently. Ability to work independently and as a member of a team.
Preferred Qualifications:3-5 years of experience working with cancer-related data. Understanding of machine learning concepts and methodologies; experience applying or supporting machine learning approaches in healthcare is an asset.
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