Data Scientist

• Frame ambiguous revenue-cycle problems as tractable modeling problems and own them end to end such as claim and invoice viability scoring, denial and underpayment prediction, revenue/ARR prediction, and work prioritization from hypothesis to a validated, monitored result in production
• Build and validate models with statistical rigor: thoughtful feature design, sound handling of missingness and class imbalance, calibration, honest evaluation against simple baselines, and a clear treatment of uncertainty
• Design, build, and maintain the data pipelines feeding these models integrating with source collections, billing, and EHR systems and internal data stores, and handling data collection, cleansing, field mapping, and payer/plan normalization with a bias toward reliability and data quality
• Establish and own MLOps practice: model monitoring and drift detection (e.g., population stability), calibration and threshold selection, benchmarking against simple baselines, explainability (e.g., SHAP and interpretable coefficients), experiment and version tracking, and model documentation such as model cards
• Partner with subject-matter experts to encode business-rule and heuristic logic alongside statistical models where that produces a more accurate or more defensible result
• Gather and document business requirements for revenue-cycle initiatives, including workflows, decision points, stakeholder objectives, operational constraints, success criteria, data availability, and underlying business assumptions, and maintain traceability as those requirements evolve.

Senior AI Data Scientist

• Developing effective anomaly detection metrics and monitoring tools to identify potential fraud.
• Analyzing data to detect suspicious activity, identify fraud patterns, and surface emerging trends.
• Partnering closely with cross‑functional teams to share insights and continuously improve fraud mitigation processes.
• Performs analytical tasks on vast amounts of structured and unstructured data to extract actionable business insights.
• Participates in the data gathering, data processing and data mining of large and complex datasets.
• Develops algorithms using advanced mathematical and statistical techniques like machine learning to predict business outcomes and recommend optimal actions to management.
• Runs analytical experiments in a methodical manner to find opportunities for product and process optimization.
• Assists in the presentation of business insights to management using visualization technologies and data storytelling.

Data Scientist – Enterprise Fraud

• Responsible for mining big data, interpreting and analyzing complex data sets
• Develops and deploys predictive models to analyze company performance and improve customer experiences
• Applies statistical techniques and machine learning algorithms to identify trends within Mazda data
• Collaborates closely with data engineers, analysts, and business stakeholders to integrate data-driven solutions

• Work as part of a team to build predictive models and applied LLMs designed to support the Kemper claims operation in areas such as fraud detection, subrogation identification, vehicle damage severity, adjuster triage and claims quality management.
• Develop and automate predictive modeling processes that can be deployed through the organization to solve reoccurring analytics needs.
• Contribute to the improvement of internal modeling capabilities and data infrastructure by suggesting and applying tweaks to existing processes.
• Monitor deployed solutions and escalate as needed.

• You will develop new models and algorithms that allow our clients to make strategic decisions for promotion and pricing
• You will implement new features in Cognira’s forecasting platform
• You will be responsible for implementing forecast and strategy solutions using Cognira’s forecasting platform
• You will apply a wide range of statistical and quantitative analysis in practical and valuable ways to real-world business problems
• You will be processing, cleansing, and verifying the integrity of data used for analysis
• You will help create and maintain rich visualizations through data interpretation and analysis
• You will write maintainable and well-documented code
• You will learn and work with subject matter experts from other teams and business representatives from our clients

Senior Data Scientist

• Develop, implement, and maintain data governance frameworks specific to AI initiatives
• Collaborate with cross-functional teams to design, deploy, and optimize AI solutions
• Monitor emerging AI technologies and regulatory changes
• Facilitate communication among technical teams, program managers, and government stakeholders
• Lead risk assessment and mitigation efforts related to AI data privacy

Artificial Intelligence Solutions Lead, Data Governance

• Maintain a safe and secure work environment through active participation in site planning with job hazard analysis reviews and approvals, on-site tool box talks, and conducting site walks with the project team.
• Responsible for execution of the project throughout its life cycle.
• Ensure compliance with state, local, and federal legal requirements.
• Responsible for conducting and documenting operational project meetings (Hand-Off Meeting, Kick-Off Meeting, etc.) with appropriate parties in a timely manner.
• Negotiate and prepare vendor and subcontractor agreements in alignment with project estimates.
• Evaluate submittals to ensure they reflect the contract drawings, specifications.
• Coordinate quality assurance and quality control with appropriate personnel to meet company and project requirements.
• Responsible for managing the coordination of project’s scope through the Building Information Management Department with assistance from the appropriate quality control personnel and project supervision.
• Perform the financial set up of the project including equipment, labor, materials, other direct job costs, and subcontracts for leadership approval.
• Conduct monthly project cost reviews, capturing any deviations from the financial setup or schedule early while keeping all project team members and leadership informed to aid in beating project schedule and budgetary goals.
• Manage the project’s Accounts Receivables by developing the pay applications schedule of value to ensure timely and accurate billings and collection of our payments to maintain a positive cash position.
• Manage the project’s Accounts Payables by working with the Accounting and Legal Departments to ensure that subcontractors and vendors are paid in a timely fashion and all risks associated with payments are mitigated.
• Responsible for obtaining and coordinating appropriate lien waivers, state notifications, tax documentation, and permits for the project.
• Responsible for ensuring the project’s budget and scope are always aligned through the change management process.
• Daily project requirements include but not limited to: attend jobsites meetings, schedule deliveries, perform project site documentation, review jobsite labor reports with supervision, tool/equipment rental requisition, coordinate job progress, material and consumables requisitions, preparation and tracking of request for information, assist supervision with permitting and inspections, approve vendor/sub invoices, status reports, monthly billings and collections.
• Build and maintain strong relations with internal team members, the customer, subcontractors, and vendors.
• Monitor status of Risk Management items, including Notices to Proceed, contract agreement (including change orders), insurance policies, and bonds.
• Responsible for close-out documents including operations and maintenance manuals, equipment start-up documents, as-built requirements, warranty letters, and Owner-training items.
• Responsible for managing all warranty work falling within contractual obligations if applicable.
• Coordinates the start-up and commissioning processes with the project team members, subcontractors, the customer, and any other external party involved to ensure compliance in a timely manner.
• 50+ hour work week

Data Center Project Manager

• Advise clients on end-to-end solutions across data strategy, data management, and data quality.
• Support business development, methodology development, team leadership, and delivery execution for data governance assessments and implementations.
• Design and develop data governance technology solutions across data quality, data dictionaries, business glossaries, data standards, and data policies.
• Define organizational design, governance processes, policies, roles, responsibilities, success metrics, and operating models for client data programs.
• Build roadmaps covering technology, culture, governance, processes, skills, competencies, data quality, analytics, and data management maturity.
• Lead and collaborate with data governance teams to create business cases, resource plans, implementation roadmaps, and enterprise data system assessments.
• Assess current-state environments, identify client pain points and requirements, and help define a practical vision and strategy for becoming a data-driven organization.
• Advise clients as a trusted partner and provide thought leadership on data governance, data management, cloud transformation, AI readiness, and related best practices.
• Manage resources, budgets, project delivery, team development, training, mentoring, and performance assessment.

Data Strategy and Governance Manager

• Analyze equipment, maintenance, and operational data to identify reliability risks, failure trends, bad actors, and improvement opportunities.
• Develop and maintain reliability dashboards, scorecards, and key performance indicators that support data-driven decision-making.
• Conduct statistical analyses, reliability studies, and failure trend evaluations to support reliability improvement initiatives.
• Provide actionable insights to reliability, maintenance, engineering, and operations teams to improve asset performance.
• Support the development and optimization of predictive maintenance programs for critical manufacturing assets.
• Analyze condition monitoring data, asset criticality rankings, and maintenance strategies to improve equipment reliability and maintenance effectiveness.
• Develop asset health monitoring methodologies, predictive models, and leading indicators of equipment failure.
• Evaluate the effectiveness of predictive maintenance technologies and identify opportunities for continuous improvement.
• Develop predictive analytics, automated alerts, equipment health scoring systems, and reliability monitoring tools.
• Utilize manufacturing, maintenance, and historian data to identify patterns, risks, and performance improvement opportunities.
• Support reliability improvement initiatives through root cause analysis, performance tracking, and benefit realization reporting.
• Develop reporting and analytics that support reliability governance, prioritization, and investment decisions.
• Promote best practices and standard methodologies across reliability programs and manufacturing sites.
• Quantify operational and financial benefits resulting from reliability and maintenance improvements.

Senior Reliability Data Scientist

• Provide subject matter expertise across facility-related data management processes and technical projects.
• Ensure data accuracy, integrity, and accessibility across facility operations, maintenance, and asset management systems.
• Serve as the subject matter expert (SME) for facility data management practices, standards, and lifecycle governance.
• Develop, maintain, and enforce data governance policies to ensure compliance with organizational, federal, and regulatory requirements.
• Establish and manage data quality assurance protocols, conduct routine audits, validation checks, and corrective actions to maintain data accuracy, consistency, and reliability.
• Ensure digital records, data structures, and metadata adhere to all applicable compliance and retention requirements.
• Oversee and validate the implementation and ongoing utilization of facility management systems, ensuring accurate tracking of assets, maintenance activities, and space utilization.
• Support the design, development, configuration, and integration of hardware, software, cloud components, and user interfaces as they relate to facility data workflows.
• Collaborate with IT, facility operations, and data teams to integrate data systems, streamline workflows, and ensure end-to-end alignment across platforms.
• Participate in system configuration, documentation updates, version control, and system testing, including developing test cases and reviewing results.
• Maintain system functionality by monitoring operational performance, identifying issues, and supporting the resolution of data-related errors or configuration gaps.
• Generate dashboards, metrics, and analytical reports using tools such as Power BI to support operational decisions, planning activities, and performance monitoring.
• Define, configure, and maintain Key Performance Indicators (KPIs) aligned with organizational strategy and reporting requirements.
• Analyze facility-related data to identify operational trends, inefficiencies, and opportunities for process optimization or cost savings.
• Participate in facility team meetings related to asset management, work management, and data-driven initiatives.
• Work closely with facility operations, IT, and data teams to ensure accurate data capture, smooth workflow execution, and system alignment with operational needs.
• Assist in developing and refining system requirements for facility data, asset tracking, and maintenance workflows, translating stakeholder needs into actionable technical or reporting updates.
• Provide user support and training for facility management teams on data entry protocols, reporting tools, system workflows, and data governance practices.
• Develop and maintain detailed documentation and standard operating procedures related to data quality processes, system configurations, reporting procedures, and operational guidelines.
• Promote and reinforce consistent data entry and governance standards and routine quality assurance practices across teams.
• Stay current on best practices, emerging technologies, and industry trends related to facility data management, analytics, and system integration.
• Recommend and implement enhancements to improve data accessibility, reporting accuracy, system efficiency, and overall data maturity.

Facility Data Manager

• Use Python or R to explore data, perform analysis, and contribute to prototyping analytical approaches within established workflows.
• Support the development, validation, and monitoring of interpretable machine learning models using sound statistical and modeling techniques.
• Contributes to the delivery of analytical solutions from prototype through testing and deployment, working closely with senior team members and engineering partners.
• Support communication across business, product, and engineering teams by helping gather requirements and clearly explain analytical work and results.
• Assist in defining and tracking performance metrics to measure solution effectiveness.
• Actively learn and adopt best practices in model development, code quality, and production deployment.

Associate Data Scientist – Corporate & Institutional Banking

• Partner with Human Resources and Enterprise Technology leadership to identify and prioritize data analytics opportunities
• Drive the implementation of time-bound data solutions aligned with project goals and stakeholder requirements
• Apply expertise in data science, natural language processing, and Generative AI to deliver tangible solutions within our Technical Human Resources environment
• Develop and deploy sophisticated data science models and algorithms to address specific business challenges
• Break down complex scientific projects into manageable hypotheses and deliverable solutions
• Collaborate across teams to ensure relevance and impact of your work
• Provide expert guidance on Generative AI implementation best practices

Senior Data Scientist, People Analytics

• Under limited supervision, the Data Scientist works closely with business stakeholders to understand how data can be used to achieve their goals.
• Designs data modeling processes, creates algorithms and predictive models and helps analyze the data and shares insights.
• Critical thinking and problem-solving skills are essential for interpreting data.
• Must have a passion for machine-learning and research.
• This position will be tasked with helping the company analyze trends to make better data-based decisions.
• Analyzes large amounts of information to discover trends and patterns.
• Designs machine learning models for predictive analytics.
• Presents information using data visualization techniques.
• Undertakes preprocessing of structured and unstructured data.
• Identifies valuable data sources and automates collection processes.
• Performs other duties as assigned.

Data Scientist, Finance

• Manages and builds systems of models to analyze diverse big data sources
• Manages and participates in developing, testing and validating models that drive business value
• Assists with identifying and interpreting insights from data
• Direct leadership of assigned data science team
• Manage and participate in working with large data sets to solve unstructured problems using different analytical and statistical approaches
• Manage the sourcing, ingesting, and cleaning of data sets in preparation for analysis
• Ensure data is stable, accounting for complex data drift in development and production
• Manage the building of econometric, statistical and machine learning models for various problems
• Manage committing of complex coding into model repository
• Lead the selection and refinement of models taking into account performance, reliability and stability metrics
• Develop model refinement educational materials and deliver related training for data users
• Create outputs from multiple models for business discussions
• Lead stakeholder meetings to discuss concerns, opportunities and production challenges
• Review own and assigned team’s code to ensure it is efficient, accurate, and using best practices
• Understand and adhere to the Company’s risk and regulatory standards, policies and controls

Lead Data Scientist – Compliance Analytics

• Use Python or R to explore data, perform analysis, and rapidly prototype analytical approaches within repeatable workflows.
• Design, develop, validate, and monitor interpretable machine learning models using sound statistical and modeling techniques.
• Own the end‑to‑end delivery of analytical solutions—from prototype through testing, validation, and scalable production deployment—collaborating closely with engineering and testing partners to ensure production readiness.
• Act as a key communication bridge across business, product, and engineering teams to gather and document requirements, clearly communicate analytical solutions, and ensure business needs are accurately implemented.
• Define and track performance metrics to measure solution effectiveness and business impact.

Data Scientist – Corporate & Institutional Banking

• Performs analytical tasks on structured and unstructured data to extract actionable business insights.
• Participates in the data gathering, data processing and data mining of large and complex datasets.
• Develops algorithms using advanced mathematical and statistical techniques like machine learning to predict business outcomes and recommend optimal actions to management.
• Runs analytical experiments in a methodical manner to find opportunities for product and process optimization.
• Assists in the presentation of business insights to management using visualization technologies and data storytelling.
• May partner with Data Architects, Data Analysts, Data Engineers and Visualization Experts to develop data-driven solutions for the business.

Data Scientist, Gen AI Governance

• Provides financial and regulatory reporting and analyses to maintain adequate controls over the financial and regulatory reporting processes
• Responsible for running complex business performance, risk and operational analytics
• Develops analytical methods/models to assess market, credit and/or operational risk of new and existing financial products
• Analyzes large datasets to improve risk adjusted returns, deliver profitable growth, and communicate conclusions
• Synthesizes analytical results to develop strategies that improve lending decisions and manage risk
• Applies predictive models and other tools to develop and execute appropriate segmentation and targeting for acquisition
• Works with business, credit, data, and model development partners to design and monitor test designs and analytical reporting

Fraud Data Scientist – Card Transactions

• Serve as the primary point of contact for At Work-related data and analytics needs
• Partner with CAPTRUST’s centralized data team to prioritize, design, and improve data solutions supporting At Work
• Help establish consistent definitions, metrics, and reporting standards across the At Work practice
• Improve data quality, clarity, and usability over time
• Support and enhance internal and client-facing reporting used by At Work teams
• Work closely with Operations to understand recurring reporting needs and reduce one-off or manual efforts
• Develop dashboards and reports that support operational planning, advisor effectiveness, and leadership decision-making
• Translate reporting outputs into insights that inform action, not just describe activity
• Develop qualitative and quantitative measures of client and participant engagement, trust, and relationship health
• Help establish ongoing feedback loops across the client and participant lifecycle, beyond episodic surveys
• Identify trends that signal strengthening relationships, stagnation, or potential breakdowns
• Support analysis related to the annual financial wellness survey and other experience-related initiatives
• Analyze patterns associated with strong long-term outcomes such as sustained engagement, retention, and expansion of advisory relationships as needs evolve
• Partner with leadership and Operations to identify where additional investment of time or resources may be needed
• Help reduce reliance on anecdotal feedback through clearer, shared insight

Data Scientist jobs in Alabama

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Senior AI Data Scientist

Data Scientist – Enterprise Fraud

Data Scientist

Data Scientist

Senior Data Scientist

Artificial Intelligence Solutions Lead, Data Governance

Data Center Project Manager

Data Strategy and Governance Manager

Senior Reliability Data Scientist

Facility Data Manager

Associate Data Scientist – Corporate & Institutional Banking

Senior Data Scientist, People Analytics

Data Scientist, Finance

Lead Data Scientist – Compliance Analytics

Data Scientist – Corporate & Institutional Banking

Data Scientist, Gen AI Governance

Fraud Data Scientist – Card Transactions

Data & Analytics Lead