Top Entry Level Analytics Jobs

Analyze and resolve pricing control exceptions involving fees, interest, refunds, and servicing activities. Review processes, build automated analytical workflows, develop reporting, and apply anomaly detection and responsible generative AI concepts. Partner with analysts, managers, product, operations, analytics, and technology teams to improve monitoring, documentation, and controls. Communicate findings clearly, manage multiple workstreams, and drive continuous improvement across card pricing operations.

Develops automation, data pipelines, databases, analytics, machine learning models, reporting tools, and web applications for package technology quality assurance. Analyzes manufacturing, product, and reliability data to support risk assessment and root-cause investigations. Collaborates with engineering teams, communicates findings, trains users, maintains software and reports, and shares analytics best practices across global teams.

Leads executive-facing analytics for Home Lending Product Strategy, framing hypotheses, measuring impact, evaluating tradeoffs, and delivering recommendations and business cases. Coordinates analysts across matrixed teams, partners with Strategy, Product, Marketing, Finance, Originations, and Technology, conducts market and competitor research, and translates complex analysis into concise insights for senior decision-makers.

This co-op develops analytical solutions for fulfillment performance reporting. Responsibilities include understanding fulfillment processes, consulting on data pipelines, developing data models and dashboards, analyzing trends, and providing insights and recommendations. The role collaborates with product managers, visualization analysts, data scientists, IT, technical teams, and fulfillment operations.

Collaborate with product managers, analysts, data scientists, IT, technical teams, and fulfillment teams to develop analytics solutions. Analyze fulfillment processes and data, consult on data pipelines, build data models and dashboards, and provide reporting to help fulfillment and brand operations teams improve performance and decision-making.

Designs and implements workforce analytics products, including models, reports, workflows, and dashboards. Partners with People Leaders and stakeholders to frame analytical questions, analyze workforce data, develop hypotheses, and communicate actionable findings. Responsibilities include predictive modeling, statistical analysis, data cleansing, ETL development, data governance, integrating data sources, and supporting workforce planning and compensation analysis.

Support client projects by structuring business problems, assessing data, selecting analytical approaches, conducting analyses, building and validating models, and translating findings into recommendations. The role involves quantitative consulting across industries, collaboration with clients and project teams, and potential work in AI, credit risk, sustainability, optimization, GenAI, and supply chain analytics. Business fluency in German and English and willingness to travel are required.

Supports client projects by structuring analytical problems, assessing data, selecting analytical methods, conducting analyses, building and validating models, and translating results into recommendations. Work may involve AI, predictive modeling, portfolio decarbonization, credit risk, optimization, chatbots, and supply-chain analytics. The role also includes client collaboration, communicating complex concepts clearly, and contributing to project teams across industries.

Join LSEG’s 12-month graduate programme in Data and Analytics, developing foundational knowledge of financial markets, data, and business operations. You may align with Analytics or Data and Feeds based on your skills and interests. Responsibilities include analyzing information, drawing conclusions, proposing solutions, collaborating on projects, and adapting in a fast-paced environment. The role offers permanent employment, professional development, industry qualifications, hybrid working, and participation in global induction and assessment activities.

Supports the design, validation, and delivery of data and AI solutions. Responsibilities include gathering requirements, exploring and reconciling complex datasets with SQL and Python, testing data pipelines and semantic models, investigating data quality issues, documenting data lineage, developing Power BI dashboards, and contributing to AI data agents using Microsoft Foundry and Fabric.

Own subscription marketing and lifecycle analytics for acquisition, conversion, retention, churn, CRM campaigns, engagement, attribution, cohorts, and spend efficiency. Write SQL, build dashboards, investigate data quality issues, evaluate offers and experiments, and translate findings into recommendations for marketing and cross-functional stakeholders.

Serves as the technical and client-facing link between Inca Digital’s data engine and high-stakes financial and government clients. Conducts OSINT, web scraping, enhanced due diligence, blockchain and crypto investigations; analyzes large datasets; audits and synthesizes technical findings into intelligence reports; briefs executive stakeholders; and coordinates cross-functional teams to align products with client needs.

Partners with Product leaders to solve ambiguous business problems through forecasting, modeling, segmentation, cohort analysis, scenario analysis, dashboards, and executive insights. Owns analytical initiatives from problem framing through implementation and measurable outcomes. Uses SQL across large enterprise datasets and applies generative AI and LLM tools to analyze structured and unstructured data, while validating AI-generated outputs. Prioritizes analytics requests, improves recurring workflows, and communicates recommendations to senior stakeholders.

Manages HR data, analytics, reporting, dashboards, pipelines, integrations, and automation. Supports compensation, performance management, workforce planning, HRIS maintenance, and employee listening initiatives. Builds data models and AI integrations, audits data integrity, develops actionable insights, and presents findings to HR and executive leadership. Requires strong compensation expertise, advanced Excel, HRIS and BI experience, scripting, data modeling, and handling confidential information.

As an Analyst, you will conduct analyses for Hive's offerings, support project delivery, collaborate with teams, and communicate project outcomes.

Collects, consolidates, cleans, analyzes, and visualizes experimental data from analytical equipment, test rigs, and pilot plants. Performs statistical analysis, anomaly detection, experimental design support, equipment validation, and QA/QC activities. Presents findings to technical leadership, identifies process improvements, and helps extract actionable insights from structured and spectral data.

Teach undergraduate and graduate information systems and analytics courses, including course preparation, student assessment, grading, advising, and office hours. Participate in departmental, university, professional, and community service, contribute to student learning assessment, and maintain professional activity through scholarship, conferences, pedagogy, or industry practice.

Analyze complex business problems, develop creative data-driven solutions, and collaborate cross-functionally to deploy initiatives that improve decision-making, customer outcomes, and company growth. The role offers exposure to consumer lending, digital engagement, workflow optimization, product development, and strategic analytics while developing technical, communication, teamwork, and leadership skills.

The 24-month Analytics Leadership Development Program Associate role includes three eight-month rotations across finance, risk management, interactive marketing, technology, and data management. Responsibilities may include participating on solution development teams, improving business processes, troubleshooting issues, and performing data analysis and reporting. Participants receive on-the-job training, leadership exposure, and networking opportunities, with potential placement in a permanent role after completing the program.

A 24-month Analytics Leadership Development Program involving three eight-month rotations across Finance, Risk Management, Interactive Marketing, Technology, and Data Management. Responsibilities may include project-based solution development, process enhancements, troubleshooting, data analysis, and reporting. Participants receive on-the-job training, leadership exposure, and opportunities to transition into permanent roles.

Eight-week summer internship supporting client-facing analytics projects. Responsibilities include analyzing large, complex datasets, developing and validating statistical models, and presenting and documenting findings in client-ready formats. The role requires collaboration with Oliver Wyman teams and clients, strong communication, independent execution, and practical problem-solving. The position is based in Raleigh, North Carolina, with some potential travel to client sites.

Analyze multiple data sources, translate complex findings into actionable business insights, and communicate recommendations to cross-functional teams. The role supports Brand Management, Finance, Sales, and other partners by delivering real-time insights, influencing business decisions, solving ad hoc problems, and developing scalable analytics solutions that improve business results.

Uses consumer, shopper, market, and retail analytics alongside qualitative research to uncover insights, inform business strategy, evaluate advertising and promotional investments, and influence product, pricing, media, retail, and go-to-market decisions. Partners cross-functionally with Brand Management, Finance, Sales, R&D, and other teams to communicate findings, identify growth opportunities, and drive action.

Analyze mobile application data using Adobe Mobile Analytics, including iOS, Android, and hybrid app analytics. Utilize Sitecatalyst and Omniture for insights.

Monitor KPIs and business health across customers, segments, and systems; identify anomalies and investigate root causes across data, operations, and product changes. Build dashboards, alerts, and monitoring systems, collaborate with Engineering, Operations, and Product on issue triage, and create documentation and playbooks that improve response times and data quality practices.