Data Scientist

• Build statistical and machine learning models to improve content discovery and user engagement
• Work closely with ML engineers to translate models and insights into production systems
• Apply analytical skills to dive deep into data and find key business insights
• Apply causal inference methods to understand the impact of potential product changes
• Define and build new ML features using text and multimodal embeddings and GenAI
• Validate offline learnings with online outcomes through A/B testing; design, execute, and analyze experiments to prove product change attribution

Senior Data Scientist

• Own the roadmap for Pricing & Revenue Science
• Lead development and application of models explaining and forecasting demand, conversion, price sensitivity, utilization, and revenue performance
• Design pricing and business experiments
• Build demand and revenue forecasts
• Develop optimization approaches balancing conversion, margin, and capacity
• Create analytical frameworks measuring geography, mileage, seasonality, inventory availability, customer segment, competitive conditions, and other business drivers
• Establish standards for causal measurement, model validation, data quality, reproducibility, monitoring, and post-deployment performance
• Partner with Product, Engineering, IT, Finance, Operations, Marketing, and other business teams
• Translate analytical work into scalable decision tools and operating processes
• Lead root-cause analysis when performance shifts
• Communicate recommendations to senior leaders
• Coach a high-performing team of data scientists and analytical professionals
• Directly manage the Pricing & Revenue Science Team, including Data Scientists and pricing/revenue analytics professionals
• Make hiring, performance management, compensation, and termination decisions
• Set team priorities and analytical standards
• Oversee vendors and partners as applicable
• Report to the Vice President, Digital

Senior Manager – Data Science

• Develop and support optimization models for capacity planning, routing, scheduling, and resource allocation
• Formulate business problems using decision variables, objectives, and operational constraints
• Assist in root-cause analysis to identify optimization and automation opportunities across field operations
• Build, test, and maintain forecasting, regression, classification, and anomaly-detection models for operational problems
• Prepare and validate data, engineer features, and evaluate model results
• Build reproducible data pipelines and automate recurring analyses, model runs, and reporting
• Contribute to shared tooling, frameworks, and standards so solutions are repeatable
• Maintain data models in Snowflake used by analysts and downstream tools
• Create dashboards and decision-support tools that make results actionable
• Document logic, methodology, and assumptions alongside models, tools, and pipelines
• Present findings and limitations in plain language to the team and operational stakeholders
• Report to the Director, Operations Data Science & AI and partner with engineers and operational stakeholders

Data Scientist II

• Design, develop, and evaluate moderately complex predictive models and advanced algorithms
• Identify meaningful insights from large data and metadata sources
• Test hypotheses and models; analyze and interpret results
• Exercise sound judgment and discretion within defined procedures and practices
• Develop and code moderately complex software programs, algorithms, and automated processes
• Use modeling and trend analysis to analyze data and provide insights
• Develop understanding of best practices and ethical AI
• Transform data into charts, tables, or formats that aid effective decision-making
• Build working relationships with team members and subject matter experts
• Lead small projects and initiatives
• Document and present analytical findings to a diverse audience of stakeholders

• Design, build, tune, validate, and harden machine-learning and advanced analytic models
• Develop graph-based analytics for supplier, beneficial-ownership, and supply-chain relationship analysis
• Perform entity resolution across disparate Government, commercial, and open-source datasets
• Develop and maintain data-processing and analytic pipelines
• Build behavioral anomaly detection models for host and user activity
• Develop cascade-based analytics to prioritize large cyber-alert volumes
• Establish transparent baseline models before applying advanced machine-learning techniques
• Develop interpretable and explainable models with source-traceable outputs
• Define and evaluate model performance metrics and acceptance criteria
• Monitor feature, input, and model drift and support model retraining
• Conduct production-scale performance testing and optimize analytics for large datasets
• Package validated analytics for deployment into secure Government environments
• Support containerized deployment, including dependency management, SBOMs, vulnerability scanning, and release documentation
• Document model assumptions, methodologies, constraints, validation results, and technical configurations
• Incorporate analyst and investigator feedback into model improvement cycles
• Work closely with Morgan 6 technical leadership, data analysts, and Government mission partners

• Scale AI solutions beyond experimentation
• Automate analysis for growing volumes of call transcripts, surveys, digital feedback, and other service interaction data
• Implement generative AI, LLM, RAG, and intelligent automation opportunities
• Analyze and interpret results with some complexity
• Develop and code basic software programs, algorithms, and automated processes
• Use modeling and trend analysis to analyze data
• Collaborate with team members and participate in team projects and initiatives
• Document and present findings using effective written and verbal communication

• Identify, evaluate, and acquire external data sources supporting strategic business and product priorities
• Identify new market opportunities and data partnerships
• Build and manage data supplier relationships throughout the acquisition lifecycle
• Partner with product, analytics, legal, compliance, finance, and technology teams
• Drive supplier due diligence, data evaluations, and onboarding activities
• Negotiate commercial terms and support contract development
• Oversee transition of new data assets from evaluation through implementation and operational handoff
• Monitor supplier performance and manage governance activities, issues, renewals, and contract decisions
• Develop business cases, recommendations, and status updates for prioritization and executive decision-making
• Ensure acquisition activities align with internal policies, risk standards, and regulatory requirements

Manager, Data Acquisition

• Identify, evaluate, and acquire external data sources supporting strategic business and product priorities
• Identify new market opportunities and data partnerships
• Build and manage data supplier relationships throughout the acquisition lifecycle
• Partner with product, analytics, legal, compliance, finance, and technology teams
• Drive supplier due diligence, data evaluations, and onboarding activities
• Negotiate commercial terms and support contract development with legal and business stakeholders
• Oversee transition of new data assets from evaluation through implementation and operational handoff
• Monitor supplier performance and manage ongoing governance activities
• Address supplier issues, renewals, and contract-related decisions
• Develop business cases, recommendations, and status updates for prioritization and executive decision-making
• Ensure acquisition activities align with internal policies, risk standards, and regulatory requirements

• Develop, maintain, and enforce enterprise-wide data governance policies, standards, and procedures
• Define and document data ownership, stewardship, and accountability structures across business domains
• Establish and manage data governance frameworks, councils, and working groups
• Ensure data accuracy, consistency, completeness, security, and regulatory compliance
• Monitor and improve data quality through profiling, validation, and issue remediation
• Partner with data owners and stewards to identify, analyze, and resolve data defects and root causes
• Support master data management, metadata management, and reference data controls initiatives
• Implement and monitor controls for sensitive data handling, retention, classification, change management, and access management
• Support internal and external audits, risk assessments, and remediation planning
• Collaborate with legal, compliance, cybersecurity, IT, business stakeholders, and data owners
• Provide training and support to data owners, stewards, and end users
• Create and maintain documentation of governance frameworks, policies, procedures, and data lineage
• Research complex issues, self-learn, solve problems, and implement solutions
• Translate data-related requirements into governance solutions
• Monitor industry trends, emerging regulations, and data governance best practices
• Travel 10–20% and support onsite implementations or enterprise solution integrations as needed

Data Scientist jobs in Florida

Senior Data Scientist

Senior Manager – Data Science

Data Scientist II

Senior Data Scientist

Senior Data Scientist

Data Scientist

Manager, Data Acquisition

Manager, Data Acquisition

Lead, Data Governance