BetMGM

Marketing Analyst

• Responsible for delivery of actionable insights and analysis in response to key business questions
• Support the key Brand and Marketing teams, providing day-to-day reporting and analytics
• Analyze data from both internal and external sources to support business objectives
• Provide campaign analysis and enable data driven marketing decisions across the marketing teams
• Handle both day-to-day reactive operational requirements and larger complex projects while being able to meet deadlines
• Build and maintain datasets, reports and dashboards

Marketing Retention Analyst

Business Analyst

• Monitor player gameplay activity, deposits and withdrawals to identify suspicious or potentially fraudulent emerging patterns and recommend fraud prevention strategies.
• Utilize various data sources to design, test and implement effective fraud prevention rules.
• Assist in tuning existing fraud detection rules and thresholds to reduce false positives while improving detection rates.
• Conduct root cause analysis on fraud trends and recommend preventative measures and process enhancements.
• Support the ongoing optimization of chargeback prevention and representment processes.
• Collaborate closely with the Operations teams to resolve emerging fraud rings and enforce account actions (i.e., suspension, closure, etc.).
• Analyze betting patterns to detect irregular gameplay, arbitrage betting and coordinated activity between accounts.
• Design and build effective dashboards to support the monitoring of key fraud KPIs.

Fraud Management Analyst

Product Manager

• Lead strategic product initiatives within the Gaming portfolio, defining goals, requirements, and clear success measures.
• Drive end-to-end execution of high-impact Gaming features and capabilities, collaborating with engineering, design, analytics, operations, compliance, and external partners to ensure high-quality delivery.
• Create detailed product requirements, user stories, and acceptance criteria that balance customer needs, technical feasibility, business value, and regulatory constraints.
• Provide strong cross-functional leadership to align stakeholders, including product managers and developers from adjacent teams to resolve ambiguities and ensure momentum across initiatives with multiple dependencies.
• Analyze product performance using qualitative and quantitative inputs to identify opportunities to elevate engagement, satisfaction, and overall experience quality.
• Communicate product strategy, trade-offs, decisions, and progress effectively to senior leaders and partner teams, using data-driven narratives.
• Identify and mitigate risks early, developing contingency plans that support predictable and high-quality delivery.
• Contribute to the evolution of product management practices across the Gaming team.

Senior Product Manager, Gaming

Data Engineer

• Own the path from raw transactional and event data to trustworthy, well-modeled datasets powering BetMGM's analytics, ML, and operational systems.
• Design, build, and operate batch, micro-batch, and streaming pipelines feeding Snowflake — Prefect-orchestrated flows on ECS Fargate, dbt for transformation, Snowpipe Streaming and Kafka for event ingestion.
• Own the full dbt lifecycle (sources → staging → intermediate → marts) with model contracts, freshness SLAs, automated tests, and version-controlled documentation.
• Stand up Snowflake objects (warehouses, RBAC, resource monitors, Dynamic Tables, Iceberg tables) through Terraform — no ClickOps in production.
• Build AWS-native infrastructure for data workloads — S3, ECS Fargate, Lambda, EMR Serverless, Glue Catalog, IAM, Secrets Manager, VPC endpoints — entirely in Terraform.
• Maintain CI/CD pipelines (GitLab CI or GitHub Actions) that gate every change with linting, dbt build, unit tests, contract checks, and AI-assisted code review.
• Tune warehouse sizing, clustering, and query patterns for cost and latency; instrument credit usage via ACCOUNT_USAGE; right-size before scaling up.
• Design RBAC, masking policies, and row-access policies that satisfy a regulated operator without becoming an access bottleneck.
• Own freshness SLAs and data contracts for the gold layer; configure Monte Carlo coverage for volume, freshness, schema, and distribution; triage incidents end-to-end.
• Collaborate with analytics engineers, data scientists, and ML platform engineers on shared standards (naming, testing, observability, lineage, cost attribution).

Senior Data Engineer

Machine Learning Engineer

• Stand up and operate BetMGM's ML platform on AWS (SageMaker Training, Model Registry, Pipelines, Endpoints, Batch Transform) and Snowflake (Snowpark ML, Cortex), with Terraform-managed infrastructure.
• Build self-service scaffolds that let data scientists ship a model end-to-end without a ticket queue — cookie-cutter project templates with CI, drift monitoring, alerting, IaC, and Snowflake connectivity pre-baked.
• Design and operate batch scoring pipelines — SageMaker Batch Transform, dbt-orchestrated scoring against Snowflake, Snowpark ML — with explicit freshness and cost SLAs.
• Design and operate real-time inference paths — SageMaker real-time endpoints, Lambda + Bedrock for GenAI, API Gateway — with stated latency budgets (typically sub-100ms) and graceful degradation under load.
• Own the feature store (SageMaker Feature Store, Tecton, or Feast) with guaranteed online/offline parity — training-serving skew is treated as an incident, not a tradeoff.
• Build CI/CD for ML — model registry, automated retraining triggers, model versioning, lineage from feature → training run → deployed model → live prediction.
• Implement champion/challenger, shadow deployments, and canary releases as platform primitives so individual model teams do not reinvent them per project.
• Stand up drift detection, data quality, and model performance monitoring (Evidently, Arize, or SageMaker Model Monitor — pick one and standardize) with paging that routes to humans who can fix it.
• Own MLOps incident response — production model failures are SEV events with postmortems.
• Right-size endpoints, batch caching, request batching, and autoscaling. State cost-per-prediction targets up front and meet them.
• Integrate LLM APIs (Bedrock, Anthropic, OpenAI) into production paths — RAG pipelines, agent eval frameworks, prompt versioning, cost and latency observability.

Senior Machine Learning Operations Engineer

Business Intelligence Analyst

• Ensure compliance through the development, implementation and monitoring of effective policies and procedures as it relates to incident reporting across all streams of compliance and technical governance, for the business’ operations.
• Assist in the management of the investigation, drafting, and reporting of regulatory violations and instances of non-compliance.
• Ensure the Company’s online iGaming platform, online Sportsbook and retail Sportsbooks operations are compliant with the requirements of relevant state regulations, directives from state regulatory agencies, and obligations of land-based sportsbook.
• Assist in the creation and implementation of processes and procedures related to incident reporting, investigation and report drafting for Sports Wagering and iGaming Compliance.
• Ensure the proper operation and integrity of these processes related to Sports Wagering and iGaming Compliance.
• Directly interface with regulators in every jurisdiction and serve as a main POC in all jurisdictions for Gaming Compliance related matters.
• Maintain business as usual functions in existing jurisdictions and ensure updates are made in adherence to rule changes, regulatory directives and operational enhancements for iGaming Compliance.
• Provide oversight, quality control, and guidance in the investigation, drafting and submission of all Incident Reports and notifications as they relate to the business’ operations in Sports Wagering and iGaming while maintaining regulatory compliance.
• Continuously identify areas of opportunity for improvement in incident reporting processes and take the lead in establishing required revisions.
• Continuously monitor updates to online gaming and sports wagering regulations.
• Work cross-functionally across departments within the Company, often switching between competing priorities.

Senior Analyst, Regulatory Incident Reporting

Analyst

• Responsible for leading the development and delivery of actionable insights and analysis in response to key business questions across bonus, VIP, trading, promotions, etc.
• Efficiently manage and deliver multiple short- and long-term projects at pace
• Lead the analytics strategy within the Sports Analytics team in collaboration with the Sports SLT
• Present analytical findings and strategic recommendations clearly to senior leadership, translating quantitative outputs into actionable business narratives
• Build and maintain a range of complex datasets, reports and dashboards
• Lead analytical work to measure, evaluate and optimize bonus and promotional spend
• Design and evaluate controlled experiments (A/B and multivariate tests) to drive incremental business value
• Build agents and automations to automate manual tasks and processes
• Mentor junior analysts and contribute to a rigorous, high-performance analytics culture

Senior Sports Analyst

• Stand up and operate BetMGM's ML platform on AWS (SageMaker Training, Model Registry, Pipelines, Endpoints, Batch Transform) and Snowflake (Snowpark ML, Cortex), with Terraform-managed infrastructure.
• Build self-service scaffolds that let data scientists ship a model end-to-end without a ticket queue — cookie-cutter project templates with CI, drift monitoring, alerting, IaC, and Snowflake connectivity pre-baked.
• Design and operate batch scoring pipelines — SageMaker Batch Transform, dbt-orchestrated scoring against Snowflake, Snowpark ML — with explicit freshness and cost SLAs.
• Design and operate real-time inference paths — SageMaker real-time endpoints, Lambda + Bedrock for GenAI, API Gateway — with stated latency budgets (typically sub-100ms) and graceful degradation under load.
• Own the feature store (SageMaker Feature Store, Tecton, or Feast) with guaranteed online/offline parity — training-serving skew is treated as an incident, not a tradeoff.
• Build CI/CD for ML — model registry, automated retraining triggers, model versioning, lineage from feature → training run → deployed model → live prediction.
• Implement champion/challenger, shadow deployments, and canary releases as platform primitives so individual model teams do not reinvent them per project.
• Stand up drift detection, data quality, and model performance monitoring (Evidently, Arize, or SageMaker Model Monitor — pick one and standardize) with paging that routes to humans who can fix it.
• Own MLOps incident response — production model failures are SEV events with postmortems.
• Right-size endpoints, batch caching, request batching, and autoscaling. State cost-per-prediction targets up front and meet them.

• Own the path from raw transactional and event data to trustworthy, well-modeled datasets powering BetMGM's analytics, ML, and operational systems.
• Design, build, and operate batch, micro-batch, and streaming pipelines feeding Snowflake — Prefect-orchestrated flows on ECS Fargate, dbt for transformation, Snowpipe Streaming and Kafka for event ingestion.
• Own the full dbt lifecycle (sources → staging → intermediate → marts) with model contracts, freshness SLAs, automated tests, and version-controlled documentation.
• Stand up Snowflake objects (warehouses, RBAC, resource monitors, Dynamic Tables, Iceberg tables) through Terraform — no ClickOps in production.
• Build AWS-native infrastructure for data workloads — S3, ECS Fargate, Lambda, EMR Serverless, Glue Catalog, IAM, Secrets Manager, VPC endpoints — entirely in Terraform.
• Maintain CI/CD pipelines (GitLab CI or GitHub Actions) that gate every change with linting, dbt build, unit tests, contract checks, and AI-assisted code review.
• Tune warehouse sizing, clustering, and query patterns for cost and latency; instrument credit usage via ACCOUNT_USAGE; right-size before scaling up.
• Design RBAC, masking policies, and row-access policies that satisfy a regulated operator without becoming an access bottleneck.
• Own freshness SLAs and data contracts for the gold layer; triage incidents end-to-end.
• Direct AI coding agents as a force multiplier — writing specs, decomposing work, reviewing AI-generated PRs, and owning the architectural decisions agents cannot make.
• Partner with analytics engineers, data scientists, and ML platform engineers on shared standards (naming, testing, observability, lineage, cost attribution).

Marketing

• Responsible for leading the development and delivery of actionable insights and analysis in response to key business questions
• Efficiently manage multiple short- and long-term projects to keep on track with deadlines and continue to push the business forward
• Support the key Brand and Marketing teams, providing day-to-day reporting and analytics
• Interface with internal and external stakeholders regularly to identify areas of focus within the business
• Handle both day to day reactive and proactive operational requirements and larger complex projects while being able to meet deadlines
• Building and maintaining datasets, reports and dashboards

Marketing Acquisition Analyst

• Own day‑to‑day portfolio management for Marketing initiatives, ensuring work is aligned to strategy, prioritized against capacity, and sequenced effectively.
• Partner with Marketing leaders to identify, shape, and intake new initiatives, assessing scope, dependencies, and execution complexity.
• Maintain and actively manage the Marketing portfolio roadmap, including interdependencies, risks, and key milestones.
• Provide regular portfolio‑level reporting focused on delivery confidence, outcomes, risks, and required decisions.
• Lead priority Marketing programs from initiation through delivery, with accountability for scope, timeline, and delivery outcomes.
• Establish clear program structures, success criteria, milestones, and ownership models across participating teams.
• Drive cross‑functional alignment across Marketing, Media, CRM, Analytics, Finance, Product, Technology, and Compliance to ensure coordinated execution.
• Track delivery progress, proactively identify risks and delivery gaps, and implement mitigation plans to keep programs on track.
• Escalate delivery issues with clear context, options, and recommendations to enable timely decision‑making.
• Serve as a trusted strategic partner to Marketing leaders by enabling clear, data-informed decision-making across the portfolio.
• Actively engage senior stakeholders to understand business objectives, constraints, and trade-offs, translating them into executable program plans.
• Prepare decision-ready perspectives by framing options, implications, risks, and recommendations for portfolio and program-level decisions.
• Facilitate portfolio governance by designing and executing forums that promote clarity, accountability, and alignment—rather than process for process’ sake.
• Partner closely with Finance, BI, Analytics, and other enabling teams to support initiatives requiring new forecasting models, goal frameworks, or measurement approaches.
• Proactively identify where decisions are blocked or unclear and drive resolution by aligning the right stakeholders around a path forward.
• Ensure leadership has confidence not just in what is planned, but in what can be executed and delivered.
• Reinforce PMO standards and delivery discipline while adapting governance approaches to support Marketing’s pace, experimentation, and evolving needs.
• Contribute to a connected PMO model by collaborating with other Business Portfolio Managers on shared dependencies and best practices.

Business Portfolio Manager – Marketing

SDET

• Own and execute testing efforts for assigned features, products, or integrations
• Ensure coverage of functional, exploratory, and regression testing needs
• Collaborate with Technical Managers, Product Managers, and cross-functional teams to define testing scope, risks, and acceptance criteria for features and releases
• Perform application and platform testing on in-house and third-party integrations based on regulatory and technical requirements
• Design, implement, and maintain manual test cases and applicable automated tests
• Lead investigation, reproduction, and analysis of complex defects
• Clearly document issues with actionable detail, traceability, and context
• Review, reproduce, and validate issues raised by external testing entities or partners
• Proactively identify quality risks, gaps in coverage, or process inefficiencies and propose practical improvements
• Mentor junior testers.

Test Engineer

Data Analyst

• Conduct exploratory analyses to uncover player trends, engagement patterns, and friction points
• Translate findings into clear insights to support product development and UX initiatives
• Apply core statistical techniques such as: Hypothesis testing, Regression analysis (linear/logistic), Clustering or segmentation logic
• Assist in the setup, monitoring, and analysis of A/B tests and product experiments
• Contribute to test design and metric definition in collaboration with product managers
• Summarize results and support interpretation of causal impacts
• Build and maintain dashboards using tools like Dash, Streamlite or Power BI
• Present insights in recurring team syncs or product planning sessions
• Document analyses and findings in a clear, stakeholder-friendly format

Marketing Retention Analyst

Fraud Management Analyst

Senior Product Manager, Gaming

Senior Data Engineer

Senior Machine Learning Operations Engineer

Senior Analyst, Regulatory Incident Reporting

Senior Sports Analyst

Senior Machine Learning Operations Engineer

Senior Data Engineer

Marketing Acquisition Analyst

Business Portfolio Manager – Marketing

Test Engineer

Product Data Analyst