4 Geospatial Analysis Jobs

At PwC, the focus of individuals in data and analytics engineering is to utilize advanced technologies and techniques for designing and developing robust data solutions for clients. They are instrumental in converting raw data into actionable insights, facilitating informed decision-making, and propelling business growth. Those specializing in data science and machine learning engineering at PwC concentrate on employing advanced analytics and machine learning techniques to extract insights from extensive datasets and drive data-driven decision-making. Your responsibilities will include developing predictive models, conducting statistical analysis, and creating data visualizations to tackle intricate business challenges. You will have a significant role in organizing and maintaining proprietary datasets, transforming data into insights and visualizations that steer strategic decisions for both clients and the firm. Working closely with industry leaders and various cross-functional retail and consumer advisory, tax, and assurance professional teams, you will assist in developing impactful, commercially relevant insights to integrate into thought leadership, external media engagement, demand generation, client pursuits, and delivery enablement. Preferred Knowledge and Skills: Demonstrates in-depth abilities and a proven track record of success in managing efforts to identify and address client needs: - As a critical member of a team of Retail and Consumer data scientists, you will maintain and analyze large, complex datasets to uncover insights related to consumer sentiment, future business trends/challenges, cyclical consumer events (e.g., holidays, back-to-school, Super Bowl), business strategy, pricing, promotions, customer segmentation, and supply chain optimization. - Assist in identifying new, cutting-edge datasets to enhance the firm's differentiation among competitors and clients. - Assist in building predictive models and data-led tools. - Design and implement experiments (e.g., A/B testing, market basket analysis) to assess the effectiveness of new approaches and drive continuous improvement. - Collaborate with the US team to translate analytical findings into actionable recommendations and compelling narratives. - Develop dashboards and reports using tools like Tableau, Power BI, or Looker to support self-service analytics and decision-making. - Stay abreast of industry trends, customer behavior patterns, and emerging technologies in the consumer and retail landscape. - Experience in managing high-performing data science and commercial analytics teams. - Strong SQL and Alteryx skills, proficiency in Python and/or R for data manipulation and modeling. - Experience in applying machine learning or statistical techniques to real-world business problems. - Solid understanding of key retail and consumer metrics (e.g., CLV, churn, sales velocity, basket size). - Proven ability to explain complex data concepts to non-technical stakeholders. - Experience with retail and consumer datasets such as Circana, Yodlee, Pathmatics, Similar Web, etc. - Knowledge of geospatial or time-series analysis in a retail setting. - Previous work involving pricing optimization, inventory forecasting, or omnichannel analytics.,

At PwC, the focus of individuals in data and analytics engineering is on utilizing advanced technologies and techniques to design and develop robust data solutions for clients. You will be instrumental in the transformation of raw data into actionable insights, facilitating informed decision-making and fostering business growth. As a member of the data science and machine learning engineering team at PwC, your primary responsibility will be to utilize advanced analytics and machine learning techniques to extract insights from extensive datasets, thereby driving data-driven decision-making. Your tasks will include developing predictive models, conducting statistical analysis, and creating data visualizations to address complex business challenges. Your role will be essential in the organization and maintenance of proprietary datasets, transforming data into insights and visualizations that drive strategic decisions for both clients and the firm. You will collaborate closely with industry leaders and various cross-functional Health Industries advisory, tax, and assurance professional teams to generate high-impact, commercially relevant insights. These insights will be integrated into thought leadership, external media engagement, demand generation, client pursuits, and delivery enablement efforts. Preferred Knowledge And Skills: You are expected to demonstrate in-depth abilities and a proven track record of successfully managing initiatives aimed at identifying and addressing client needs. Some of the key responsibilities include: - Contributing to the identification of cutting-edge healthcare data sources that differentiate the firm from competitors and enhance value for clients. - Designing and executing experiments to measure the effectiveness of healthcare initiatives and drive continuous improvement. - Collaborating with the US team, healthcare business stakeholders, and client teams to translate analytical findings into actionable recommendations and compelling narratives supporting decision-making. - Developing dashboards and reports using tools like Tableau, Power BI, or Looker to facilitate self-service analytics, stakeholder engagement, and regulatory reporting. - Staying informed about industry trends, patient and provider behavior patterns, and emerging technologies influencing the healthcare sector. - Managing high-performing data science and commercial analytics teams with deep healthcare domain knowledge. - Demonstrating proficiency in SQL and Alteryx, as well as Python and/or R for healthcare data manipulation, modeling, and visualization. - Applying machine learning or statistical techniques to real-world healthcare challenges, such as cost forecasting, population health management, or precision medicine. - Possessing a solid understanding of key healthcare metrics and experience with healthcare datasets from various sources. - Knowledge of geospatial or time-series analysis in a healthcare context, such as site-of-care optimization and treatment seasonality. - Previous involvement in pricing strategy, access and reimbursement modeling, value-based care analytics, or health equity assessment. In summary, as a member of the data and analytics engineering team at PwC, you will play a vital role in leveraging advanced technologies to develop data solutions, extract insights, and drive data-driven decision-making in the healthcare sector. Your contributions will be crucial in addressing complex business challenges and providing strategic support to clients and the firm.,

At PwC, the focus of individuals in data and analytics engineering is on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. Playing a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in data science and machine learning engineering at PwC will concentrate on leveraging advanced analytics and machine learning techniques to extract insights from large datasets, driving data-driven decision making. You will be involved in developing predictive models, conducting statistical analysis, and creating data visualizations to solve complex business problems. You will be crucial in organizing and maintaining proprietary datasets, transforming data into insights and visualizations that drive strategic decisions for clients and the firm. Working closely with industry leaders and various cross-functional retail and consumer advisory, tax, and assurance professional teams to develop high-impact, commercially relevant insights for thought leadership, external media engagement, demand generation, client pursuits, and delivery enablement. Demonstrates in-depth level abilities and/or a proven record of success in managing efforts with identifying and addressing client needs: - As a critical member of a team of Retail and Consumer data scientists, maintaining and analyzing large, complex datasets to uncover insights that inform topics such as consumer sentiment, future business trends/challenges, insights around cyclical consumer-related events (e.g., holidays, back-to-school, Super Bowl, etc.), business strategy, pricing, promotions, customer segmentation, and supply chain optimization. - Supporting in the identification of new, cutting-edge datasets that add to the firm's differentiation amongst competitors and clients. - Supporting in building predictive models and data-led tools. - Designing and conducting experiments (A/B testing, market basket analysis, etc.) to measure the effectiveness of new approaches and drive continuous improvement. - Partnering with the US team to translate analytical findings into actionable recommendations and compelling stories. - Developing dashboards and reports using tools like Tableau, Power BI, or Looker to support self-service analytics and decision-making. - Staying up to date and ahead of industry trends, customer behavior patterns, and emerging technologies in the consumer and retail landscape. - Having experience managing high-performing data science and commercial analytics teams. - Strong SQL and Alteryx skills and proficiency in Python and/or R for data manipulation and modeling. - Experience applying machine learning or statistical techniques to real-world business problems. - Solid understanding of key retail and consumer metrics (e.g., CLV, churn, sales velocity, basket size, etc.). - Proven ability to explain complex data concepts to non-technical stakeholders. - Experience with retail and consumer datasets such as Circana, Yodlee, Pathmatics, Similar Web, etc. - Knowledge of geospatial or time-series analysis in a retail setting. - Prior work with pricing optimization, inventory forecasting, or omnichannel analytics.,

We are currently seeking experienced GIS Analysts (3-7 years) with expertise in geospatial data correction, editing, reporting, schema mapping, calibration, and as-builting centerlines for pipelines. The successful candidates will play a crucial role in ensuring accurate and reliable geographic information systems (GIS) data for our pipeline projects. The primary responsibility of the GIS Analyst will be to analyze, interpret, and manipulate geospatial data to support pipeline operations and decision-making processes. Responsibilities: - Perform geospatial data correction and editing tasks, including reviewing and verifying data accuracy, resolving data inconsistencies, and updating database records as necessary. - Collaborate with cross-functional teams to interpret and analyze geospatial data, ensuring compliance with project requirements, industry standards, and regulatory guidelines. - Generate reports and visualizations using GIS software tools to communicate data findings, trends, and insights to project stakeholders. - Conduct schema mapping exercises to ensure seamless data integration across various GIS platforms and systems. - Assist in data calibration activities, including aligning GIS data with field survey data and addressing discrepancies or inconsistencies. - Support the as-builting process by accurately capturing and incorporating pipeline centerline data, construction updates, and related information into GIS databases. - Collaborate with field personnel, surveyors, and engineers to ensure the accurate representation of pipeline assets and associated geospatial attributes. - Participate in the development and maintenance of GIS data standards, procedures, and best practices to ensure data quality and consistency. - Stay updated on industry trends, advancements, and emerging technologies related to GIS analysis and geospatial data management. - Provide training and guidance to other GIS analysts and other team members as needed. Qualifications: - Bachelor's degree in Geography, Geomatics, GIS, or a related field. - Proven experience (min of 3 years) as a GIS Analyst, with a focus on geospatial data correction, editing, reporting, schema mapping, calibration, and as-builting for pipelines. - Proficiency in GIS software applications such as ArcGIS Pro, QGIS, or similar platforms. - Strong understanding of geospatial data principles, including spatial analysis, data modeling, and data quality assessment. - Familiarity with pipeline centerline data management and the as-builting process. Solid knowledge of GIS database management, including data extraction, transformation, and loading (ETL) procedures. - Experience in generating reports, visualizations, and maps using GIS tools and software. - Strong analytical and problem-solving skills, with the ability to interpret complex geospatial data and provide accurate insights. - Excellent attention to detail and the ability to work with precision in a fast-paced environment. - Effective communication skills, both written and verbal, with the ability to collaborate with multidisciplinary teams and present findings to stakeholders. - Proficiency in scripting or programming languages (Python, SQL) is a plus. - Familiarity with industry standards and best practices, such as PODS (Pipeline Open Data Standard) or similar data models, is advantageous. If you are a skilled and detail-oriented GIS Analyst with experience in geospatial data correction, editing, reporting, schema mapping, calibration, and as-builting for pipelines. We invite you to apply for this position. Join our team and contribute to the accurate and efficient management of geospatial data for our pipeline projects.