Top Data Jobs in Calgary
As a Data Analyst at Capco, you will analyze complex data to support data management decisions, collaborate with architects and clients to define data quality requirements, and execute data profiling activities to enhance data quality.
The Senior Data Engineer at Babylist will enhance data platforms for impactful decision-making, focusing on building and scaling ML pipelines. Responsibilities include collaborating with various teams to implement robust data engineering solutions and managing cloud data resources in AWS while maintaining a high standard of coding.
Senior Data Scientist role at Square focused on leveraging data science techniques to improve incremental revenue generation. Responsibilities include understanding data ecosystem, supporting revenue forecasting models, partnering with ML engineers, driving insights with analytics, and mentoring team members.
The Staff Data Engineer will design scalable data pipelines, optimize performance, and manage data governance while collaborating with architecture leadership. They will support BI development using tools like Looker and ensure alignment with business goals by validating requirements with stakeholders.
As a Senior Data Scientist in the Risk & Fraud organization at Cash App, you will partner with various teams to analyze large datasets, derive actionable insights, design A/B experiments, and drive strategic decisions using data science techniques. You will be responsible for monitoring key metrics, building and reporting on datasets, and communicating findings to stakeholders. The role requires a sense of urgency and a focus on customer experience impact.
As a Staff Data Scientist on the Dashboard Analytics team, you will leverage data engineering, business intelligence, and machine learning to drive data-driven decision-making and optimize seller experiences. You will collaborate with cross-functional teams, define key metrics, and communicate insights to inform strategic initiatives.
Join Cash App as a Senior Data Scientist in the Support organization. Analyze large datasets, derive insights, drive meaningful change for customer support interactions, design A/B experiments, and collaborate with cross-functional teams. Build and report metrics, create data visualizations, and communicate effectively with stakeholders.
As a Data Scientist at Square, you'll utilize statistical and machine learning techniques to support decision-making for a unified point of sale product. Responsibilities include applying data analysis, machine learning, and collaborating with cross-functional teams to create strategic insights that drive business impact.
Featured Jobs
The Senior Data Engineer will design, develop, and maintain data products for Block, focusing on cloud infrastructure, ETL/data modeling, and collaboration with various stakeholders. The role requires coding in Python and SQL, maintaining an AWS cloud platform, and mentoring team members while handling complex data projects.
The Sr Data Scientist will build enterprise-scale, cloud-native AI systems, design AI/ML models, and contribute to the entire data science process. Responsibilities include developing strategies, collaborating with peers, conducting model research, and ensuring the quality of data models.
As a Senior Data Engineer, you will design and maintain data infrastructure for Square's Marketing team, focusing on web event data. Responsibilities include building data pipelines, translating requirements for ETL processes, analyzing data sources, monitoring pipeline performance, and improving data quality and efficiency. Your work will significantly influence business decisions and reporting accuracy.
The SVP will lead the Data, AI, and Automation organization, driving a strategic transformation across global operations. They will develop and implement a comprehensive AI and data strategy, collaborate with cross-functional teams to integrate solutions, and ensure performance measures align with business outcomes, emphasizing digital innovation and talent development.
Partner with the Cash App Trust org to deliver meaningful insights about customers and identity systems. Use SQL and scripting languages to analyze complex datasets, build data visualizations, and generate reports for key stakeholders. Monitor metrics, interpret A/B experiments, and communicate findings effectively with team leads.
The Data Ingestion Lead will oversee data acquisition, ingestion, and transformation processes in the Global Pet Nutrition division. Responsibilities include optimizing data workflows, ensuring data quality and reliability, implementing compliance checks, and collaborating with various teams to enhance data capabilities.
The Senior Data Scientist will analyze large datasets, design and analyze A/B experiments, build metrics for strategy, and create data visualizations. The role involves collaboration with product management to enhance the peer-to-peer lending product and improve customer experiences through data-driven insights.
As a Senior Data Scientist in Lifecycle Marketing at Cash App, you will optimize marketing campaigns using data-driven insights, analyze large datasets, and partner closely with marketing organizations to influence roadmaps and define success metrics. Your work includes building models, conducting A/B tests, creating visualizations, and reporting on key metrics to drive strategic decisions.
The role involves developing scalable batch processing systems within the Apache Hadoop ecosystem, managing Machine Learning pipelines, and utilizing GCP for data processing. The engineer will implement DevOps best practices and collaborate effectively in a remote environment.
The Senior Data Scientist will lead data analysis and machine learning efforts to extract insights from complex datasets, drive product enhancements, and influence strategic decision-making. Responsibilities include statistical modeling, data mastery, and mentorship within a cross-functional team to deliver machine learning solutions.
The Principal Data Scientist will lead the predictive analytics practice within the Data & Analytics team, focusing on operationalizing ML models for financial metrics and customer forecasts. Responsibilities include refining time series forecast models, partnering with Data Engineering for infrastructure, and collaborating across teams to enhance model performance and address operational use cases.
As a Lead Data Engineer, you will design and implement robust data pipelines and storage solutions, optimize data processes, and enhance efficiency. You'll automate ETL processes for blockchain data analytics, utilize Snowflake for data management, and collaborate with product teams to articulate data strategies, while promoting best practices in data engineering and cloud infrastructure.
The Staff Data Engineer will design and implement scalable data pipelines and systems to solve complex problems involving data, machine learning, IoT, and cloud infrastructure. This role includes contributing to enterprise architecture and collaborating with engineers to propose technical solutions that integrate with ecobee’s broader systems.
The Senior Data & AI Specialist at IOTA will lead data analysis, model development using machine learning, and product strategy development. Responsibilities include data visualization, cross-functional collaboration, and process optimization to enhance data management and insights.
As a Mandarin Chinese Image Data Contributor, you will independently locate and upload images for specified categories, generating relevant questions or prompts for each image. Your contributions aim to enhance a Large Language Model's multilingual image interpretation capabilities.
The Senior Data Analyst, Revenue Operations will build and manage data infrastructure to support revenue growth, mentor fellow analysts, and implement data-driven strategies. Responsibilities include creating dashboards, ensuring data reliability, collaborating with stakeholders, and utilizing data for strategic decision-making.
Design, build, and maintain scalable ETL/ELT pipelines for data ingestion and transformation. Implement solutions to support analytics and machine learning, integrating diverse data sources and developing data warehousing solutions. Optimize data workflows and monitor infrastructure performance while collaborating with teams to ensure high-quality data solutions.
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