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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data & Analytics Consultant - **Company:** PMY GROUP LLC - **Location:** Culver City, CA, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, BigQuery, Cloud Engineering, Information Systems, Databases, Data Validation, Information Engineering, Data Governance, Database Queries, Decision Support Systems, Statistical Hypothesis Testing, Python (Programming Language), PostgreSQL, Machine Learning, MySQL, NumPy, Operational Data Store, Power BI, Cloud Services, Backtesting, Azure Machine Learning, SQL Databases, Tableau (Software), Google Cloud, Snowflake, Git, Pandas, Scikit Learn, Information Technology, Statistics Packages, Data Analytics, Machine Learning Operations, Looker Analytics, Software Version Control, Amazon Redshift - **Published:** October 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=bcdb33cedf60a7e4 ## About the Role To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required or preferred. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. * 4-6 years of experience in data analysis, business intelligence, applied analytics, data science, or a closely related role, with a strong track record of translating data into business outcomes. * Bachelor's degree in Data Science, Statistics, Mathematics, Business Analytics, Information Systems, Computer Science, Economics, Engineering, or a related field. * Prior experience in sports, live events & entertainment, or consulting is preferred. * Strong SQL skills and practical Python experience for data analysis and modeling, with familiarity with libraries such as pandas, NumPy, scikit-learn, statsmodels, or equivalent tools; proficiency with Power BI, Tableau, or Looker is also important. * Experience working with relational and analytical databases such as PostgreSQL, MySQL, BigQuery, Redshift, or Snowflake, including large and complex datasets. * Working knowledge of statistical analysis and applied machine learning techniques such as regression, classification, clustering, time-series forecasting, recommendation, or anomaly detection. * Familiarity with cloud analytics and machine learning platforms across Google Cloud Platform, AWS, or Azure, including tools such as BigQuery ML / Vertex AI, SageMaker, or Azure ML. * Ability to evaluate analytical and model outputs using appropriate validation methods and explain assumptions, limitations, and results to both technical and business audiences. * Strong analytical and problem-solving skills, including data quality assessment, hypothesis-driven analysis, and the ability to manage multiple priorities. * Exceptional verbal and written communication skills, with the ability to explain complex analytical concepts and recommendations to both technical and non-technical audiences. * Demonstrated initiative, ownership, and accountability in delivering high-quality analytics work; familiarity with Git or similar version-control practices is beneficial. * Familiarity with responsible AI, data privacy, and data governance practices is a plus. ## Description As a Senior Data & Analytics Consultant, you will help clients turn performance, commercial, and operational data into clear insights, better decisions, and measurable business outcomes. This role blends strong analytics and reporting with applied data science: you will explore and prepare data, identify trends and drivers, develop statistical or machine learning approaches where appropriate, and translate results into practical recommendations for technical and non-technical stakeholders. You will work across dashboards, KPI development, forecasting, segmentation, anomaly detection, and other applied analytics use cases, using cloud-native data and AI tools to improve the speed and quality of analysis. The role is designed for someone who is comfortable owning analytics end-to-end while progressively applying more advanced data science techniques to client problems., Data Analysis and Applied Data Science * Collect, clean, transform, and analyze data from multiple sources to create reliable datasets for reporting, statistical analysis, and applied modeling. * Identify trends, patterns, anomalies, and business drivers, and apply statistical or machine learning techniques where they add value to the use case. * Support forecasting, segmentation, propensity, recommendation, and other predictive analytics use cases, including validation of outputs and assumptions. * Use SQL, Python, and cloud-native analytics and AI capabilities to accelerate exploration, analysis, and repeatable analytical workflows. Dashboard, Reporting and Decision Support * Design, develop, and maintain dashboards, reports, and analytical outputs that communicate KPIs, trends, model results, and business performance. * Present analytical findings, model outputs, and recommendations to clients and stakeholders in a clear, concise, and actionable manner. * Create proofs of concept that combine reporting, advanced analytics, and AI-assisted capabilities where they can improve client decision-making. Client Engagement and KPI Development * Work directly with clients to translate business goals and operational challenges into measurable questions, KPIs, analytical use cases, and success criteria. * Partner with clients to iterate on analytical approaches, dashboards, and model outputs, building trust through clear communication and measurable results. Analytics Ownership and Insight * Own analytics outputs end-to-end, from data validation and exploratory analysis through dashboarding, applied modeling, insight generation, and stakeholder delivery. * Take accountability for the accuracy, relevance, and usability of analytical outputs, ensuring assumptions and limitations are appropriately understood. * Proactively identify opportunities to improve reporting, refine KPIs, and apply predictive analytics or machine learning where it can improve operational and strategic decision-making. * Collaborate with data engineering and AI/MLOps teams when analytical models or capabilities need to be operationalized in cloud platforms. Cross-Functional Collaboration * Collaborate with data engineers, solution architects, AI/MLOps engineers, analysts, and client teams to define data requirements and deliver analytical solutions. * Experience working directly with clients and distributed technical teams across different time zones is highly desirable. Strategy and Continuous Improvement * Support analytical experiments, backtesting, benchmarking, and measurement frameworks where appropriate to quantify performance and business impact. * Contribute to data and analytics strategy, reusable analytical methods, and continuous improvement practices across multiple projects. Additional Responsibilities * Perform additional duties assigned to support evolving business needs.