Data Scientist

Numeris is seeking a Data Scientist who will be responsible for designing, developing, validating, and monitoring advanced analytics, statistical models, machine learning solutions, and data products that enhance our products, operations, and decision-making. Working with internal and external data sources and methodologies, the Data Scientist will translate complex analytical findings into clear, actionable insights for technical and non-technical stakeholders.

What you will do

Conduct advanced data analytics, modelling, fusion/integration, and machine learning initiatives

Develop and enhance algorithms that can be deployed for sample design, weighting/calibration procedures, and improvements in production.

Contribute to the application and review of data mining, data integration and hybrid methodologies.

Review statistical and machine learning models developed internally or by external business partners, applying established research, statistical and data science standards and escalating issues as appropriate.

Support cross-functional teams by providing analytics, presenting findings, and informing team decisions.

Maintain current knowledge of statistical methods, machine learning and AI-assisted analytics.

What you have

Undergraduate or Graduate degree in Statistics, Data Science, Mathematics or related quantitative fields.

An accredited statistician or Advanced Data Science certification preferred

Up to 3 years of experience in a similar role/field

Strong expertise in applied statistics, including sampling, statistical inference, regression, imputation, model
evaluation and forecasting, supported by experience in advanced analytics, data integration and machine learning.

Proficiency in Python, SQL, or equivalent, and familiarity with reproducible analytical practices including Git,
modular development, testing and documentation.

Strong analytical, problem-solving, communication and
collaboration skills, including the ability to communicate findings to technical and non-technical audiences.

Experience working with survey, panel, weighting, audience measurement, or large-scale behavioural datasets, as
well as relational databases and modern cloud data warehouse or lakehouse platforms.

Familiarity with automated
data pipelines, workflow orchestration and distributed data processing, including working efficiently with large
datasets.

Experience implementing, deploying and scaling analytical or machine learning models in production
environments. Familiarity with natural language processing, AI-driven methodologies, Large Language Models and
synthetic population frameworks is considered an asset.

What's in it for you?