About Compugen Compugen is Canada's largest privately-owned Technology Ally. To innovate industries, transform businesses, connect communities, and drive meaningful change, we must think bigger, reach broader, and act bolder. Through knowledge, curiosity, and collaboration, Compugen helps organizations delivering experience by design. This is what it means to be human-centered and technology-enabled.
Dreaming, designing, and delivering isn't just a mantra for us - it's a way of life. We believe that technology is the conduit, but our people – they are the connection that truly makes the magic happen!
Our Culture We are a human-centered culture where we prioritize your well-being and are invested in helping you reach your fullest potential. We're not only focused on achieving our goals - we're dedicated to helping you achieve yours.
If you're relentlessly curious, driven to make a difference, and collaborative at the core, then you belong with us.
Position Overview: We are seeking an experienced Senior Data Analytical Specialist/Scientist to design, develop, and implement advanced analytics, predictive models, and machine learning solutions that support strategic business initiatives and data-driven decision-making.
The successful candidate will leverage statistical analysis, machine learning, and modern cloud-based analytics technologies to identify patterns, uncover actionable insights, and develop scalable analytical solutions. This role will focus on assessing data readiness, evaluating analytical opportunities, validating model performance, and translating complex data into measurable business value.
The ideal candidate will bring strong hands-on expertise in Python, SQL, statistical modelling, Azure Machine Learning, and Microsoft Azure AI technologies, combined with the ability to collaborate with business and technical stakeholders to deliver high-impact analytics and AI solutions.
Key Responsibilities:
- Design, develop, and implement advanced analytics, predictive models, and machine learning solutions to address complex business challenges.
- Conduct exploratory data analysis to identify trends, patterns, relationships, and opportunities for improved business performance.
- Translate business requirements into analytical approaches, defining hypotheses, modelling methodologies, and measurable success criteria.
- Develop and optimize statistical and machine learning models using classification, regression, clustering, and other advanced analytical techniques.
- Apply statistical inference, probability, experimentation, and model selection techniques to support accurate and reliable analytical outcomes.
- Perform feature engineering, data preprocessing, model tuning, and validation to improve predictive accuracy and performance.
- Develop and manage end-to-end machine learning pipelines using Azure Machine Learning and Microsoft Azure AI Foundry.
- Assess data quality, availability, completeness, and readiness to support AI and advanced analytics initiatives.
- Support enterprise data maturity assessments and identify opportunities to improve analytical capabilities, processes, and data utilization.
- Evaluate machine learning models for accuracy, reliability, bias, variance, overfitting, and overall performance.
- Develop prototypes and proofs of concept to evaluate analytical approaches and demonstrate potential business value.
- Collaborate with business stakeholders to identify, prioritize, and assess high-value AI and advanced analytics opportunities.
- Partner with Data Engineers, Data Architects, Product Managers, and technology teams to operationalize successful analytical models and solutions.
- Define, develop, and monitor key performance indicators (KPIs) to measure analytical effectiveness and business outcomes.
- Develop reports, dashboards, and data visualizations using Power BI and other analytical tools to communicate insights.
- Present complex analytical findings, model assumptions, limitations, and recommendations to technical teams, business stakeholders, and executive audiences.
- Apply Responsible AI principles, data governance standards, privacy requirements, and model risk management practices throughout the analytics lifecycle.
- Document analytical methodologies, model development processes, validation results, and technical recommendations.
Skills & Qualifications:
- Degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, or a related quantitative discipline, or equivalent professional experience.
- Extensive experience in data science, advanced analytics, predictive modelling, machine learning, and statistical analysis.
- Advanced hands-on proficiency in Python and SQL, with demonstrated experience developing, tuning, and validating machine learning models using real-world enterprise datasets.
- Deep expertise in statistics, probability, statistical inference, predictive modelling, and machine learning algorithms.
- Strong knowledge of classification, regression, clustering, feature engineering, experimentation, model selection, and validation methodologies.
- Demonstrated understanding of bias-variance trade-offs, overfitting, model accuracy, and statistical evaluation techniques.
- Strong hands-on experience with Azure Machine Learning and Microsoft Azure AI Foundry, including end-to-end machine learning pipelines for experimentation, training, evaluation, and operationalization.
- Experience developing and deploying analytical solutions within Microsoft Azure cloud environments, including Azure OpenAI and Azure ML Studio.
- Experience working with Databricks for data processing, analytics, and machine learning workloads.
- Proficiency with Power BI for data visualization, reporting, and communicating analytical insights.
- Experience using development and collaboration tools, including Visual Studio Code, Git, and Jira.
- Strong knowledge of data quality assessment, data preprocessing, exploratory data analysis, and analytical methodology.
- Experience conducting data maturity assessments, evaluating analytical capabilities, and identifying opportunities for improvement.
- Demonstrated ability to assess the feasibility, potential value, and measurable business impact of AI and advanced analytics initiatives.
- Experience developing prototypes, proofs of concept, and scalable analytical solutions.
- Understanding of Generative AI, Large Language Models (LLMs), and emerging AI technologies would be considered an asset.
- Knowledge of Responsible AI, data privacy, governance, and model risk management principles.
- Strong business acumen with the ability to translate complex analytical findings into actionable recommendations.
- Excellent analytical, problem-solving, communication, and stakeholder engagement skills.
- Proven ability to manage competing priorities, work collaboratively with cross-functional teams, and deliver high-quality solutions within established timelines.
Equity Statement At Compugen, we are committed to diversity, equity and inclusion. We actively recruit from all groups, including women, Indigenous people, persons with disabilities and members of visible minorities. We recognize the importance of removing barriers to participation and provide accommodation upon request to all applicants throughout the recruitment process. If you require an accommodation, our People & Culture representative will work with you to meet your needs in a confidential and respectful manner. We believe everyone deserves a seat at the table-and we are taking deliberate action to make this a reality.
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