Data Scientist

• Delegating work and evaluating inventory staff
• Analyzing daily cycle counts and reconciling discrepancies
• Preparing monthly reports and promoting safety
• Conducting daily startup meetings and encouraging team feedback

Data Center Team Lead

• 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

• Oversee maintenance of data infrastructure and architecture for Oil, Gas, and Remediation projects
• Drive data generation, storage, and processing initiatives while ensuring the implementation of effective data management solutions and best practices
• Maintain databases and data processing systems
• Ensure data is accessible, reliable, secure, and efficiently processed
• Combine, optimize, and manage multiple large-scale data sources
• Create automated reporting systems and data solutions
• Provide software solutions that move data from its source to internal databases, including data warehouses
• Responsible for maintenance of data pipelines to support data analysis and reporting
• Analyze complex issues and recommend solutions that improve data storage processes and methodologies
• Use judgment outside defined parameters and precedents to make decisions and resolve business and technical challenges
• Accountable for the results of assigned projects and processes
• Demonstrate strong proficiency in data programming languages
• Demonstrate strong knowledge of data optimization tools, data structures, data architecture, and data warehousing solutions
• Demonstrate strong knowledge of API integration to support data access, collection, distribution, transformation, and ELT processes
• Apply algorithmic and statistical methods, data processing tools, and AI-enabled approaches to support efficient data consumption and analysis

Data Manager – Oil, Gas, Remediation

• Lead the Build of L&W's Centralized Data Function
• Develop and Execute the Multi-Year Data Strategy
• Drive Measurable Business Value
• Establish Enterprise Data Governance and Trust
• Executive Leadership Collaboration and Strategic Influence

Head of Data

• Delegate work and responsibility to subordinates
• Oversee scheduled shifts and evaluate the working of inventory staff
• Audit daily error reports and make corrections
• Generate monthly data from WMS reports
• Prepare monthly reports on major problems and project updates
• Ensure compliance with safety, security, and quality principles

• Lead the development of evaluation frameworks for the autonomous system
• Collaborate closely with Functional Safety and Systems Engineering teams
• Ensure evaluation metrics are reliable enough to inform safety cases
• Monitor the reliability of evaluation metrics and incoming performance data over time
• Drive our approach to performance analysis using data-backed statistical methods
• Develop new statistical analysis methods to analyze AV performance data
• Partner with triage operators to turn raw disengagements into scenarios
• Use fleet and evaluation data to identify edge cases in an automated manner
• Build confidence in the evaluation framework through data-driven insights
• Establish correlation between on-road and simulation data
• Make sense of large datasets to drive insights
• Establish a self-service model for developers
• Develop new metrics, interpret trends, and investigate anomalies
• Collaborate with developers to drive action based on these results
• Serve as an advisor and influence collaborators across multiple teams
• Mentor and collaborate with fellow engineers
• Introduce the use of ML methods for performance evaluation

Senior Data Scientist, Systems Performance

• 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

• Oversee the design, development, and maintenance of data infrastructure and architecture for Oil, Gas, and Remediation projects.
• Drive data generation, storage, processing, and warehousing initiatives.
• Ensure the implementation of effective data management solutions and best practices.
• Develop, construct, test, and maintain databases and large-scale data processing systems.
• Ensure data is accessible, reliable, secure, and efficiently processed.
• Combine, optimize, and manage multiple large-scale data sources.
• Create automated reporting systems and data solutions.
• Provide software solutions that move data from its source to internal databases, including data warehouses.
• Implement and maintain data frameworks and architectures.
• Develop and maintain data pipelines to support data analysis, reporting, and business intelligence.
• Analyze complex issues and recommend solutions that improve data warehousing processes and methodologies.
• Use judgment outside defined parameters and precedents to make decisions and resolve business and technical challenges.
• Be accountable for the results of assigned projects and processes.
• Demonstrate strong proficiency in data programming languages.
• Demonstrate strong knowledge of data optimization tools, data structures, data architecture, and data warehousing solutions.
• Demonstrate strong knowledge of API (Application Programming Interface) integration to support data access, collection, distribution, transformation, and ELT (Extract, Load, Transform) processes.
• Apply algorithmic and statistical methods, data processing tools, and AI-enabled approaches to support efficient data consumption and analysis.

Data Manager, Oil, Gas, Remediation

• Utilize statistical, mathematical, and predictive modeling techniques to analyze complex datasets
• Develop algorithms that support research initiatives
• Apply programming skills using tools such as SAS, SQL, SPSS, R, Python, MATLAB, or similar languages to perform data analytics and generate insights
• Assist Principal Investigator in transforming data to support research questions
• Identify and implement process improvement opportunities to enhance efficiency in data extraction, reporting, programming, and operations
• Communicate and present research findings as needed

Research Data Scientist I

• Leverage data science, machine learning, and advanced analytics to solve business problems and provide actionable insights.
• Plan and complete complex projects using statistical applications, big data analyses, and machine learning.
• Develop innovative analytic solutions, models, and automation processes to support commercial strategy.
• Build and support data models, algorithms, and statistical analyses for meaningful insights.
• Stay updated on emerging data engineering technologies and trends, evaluating and implementing appropriate tools.
• Collaborate with the BI team to improve reporting and visualizations for collaborators.
• Partner with the Enterprise Data & Insights team to develop and deploy data science models.
• Provide analytical commentary on industry trends and impacts.
• Present recommendations and results clearly to various audiences.
• Measure and report the impact of deployed models on business outcomes.

• 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 & Analytics Lead

• Lead the design, development, deployment, and optimization of machine learning, predictive analytics, and AI-powered solutions.
• Translate business challenges and opportunities into analytical approaches, model specifications, and measurable success criteria.
• Apply advanced statistical analysis, machine learning techniques, and data science methodologies to solve complex business problems.
• Analyze large, complex datasets to identify trends, patterns, opportunities, and actionable insights.
• Develop and maintain model documentation, technical specifications, and implementation plans.
• Stay current with emerging technologies, tools, and best practices in data science, machine learning, and artificial intelligence.
• Design and execute comprehensive validation and evaluation strategies for machine learning and generative AI solutions.
• Develop benchmarking frameworks and success metrics to assess model performance, reliability, and business impact.
• Evaluate model quality using quantitative and qualitative measures, including accuracy, precision, recall, robustness, latency, and business outcome metrics.
• Assess generative AI applications for response quality, grounding, relevance, consistency, and hallucination risk.
• Identify and mitigate risks related to bias, fairness, explainability, privacy, and model reliability.
• Perform model validation, testing, and performance assessments prior to production deployment.
• Establish monitoring processes and evaluation methodologies to ensure continued model effectiveness and alignment with business objectives.
• Design, execute, and analyze experiments, including A/B tests and statistical studies, to measure product and business outcomes.
• Define key performance indicators and success metrics for machine learning and AI initiatives.
• Measure and communicate the impact of analytical solutions through statistical analysis and quantitative methods.
• Partner with stakeholders to define hypotheses, success criteria, and decision-making frameworks.
• Use experimentation and data-driven insights to guide product, operational, and strategic decisions.
• Collaborate with Engineering and Data Engineering teams to implement, operationalize, and scale models in production environments.
• Monitor deployed models for performance degradation, model drift, data quality issues, and changing business conditions.
• Recommend retraining, optimization, or replacement strategies based on model performance and evolving business needs.
• Support the creation of scalable, maintainable, and reliable AI and machine learning solutions.
• Ensure model deployment processes align with engineering best practices and operational requirements.
• Partner with Product, Engineering, Analytics, and business stakeholders to prioritize opportunities and deliver high-impact solutions.
• Communicate complex analytical findings and technical concepts to both technical and non-technical audiences.
• Present recommendations, insights, and model performance results to leadership and project teams.
• Support technical reviews, project planning, and delivery activities across cross-functional initiatives.
• Contribute to knowledge sharing, documentation, and best practices within the data science organization.
• Provide technical guidance and mentorship to junior team members and peers as needed.

Data Scientist jobs in Nevada

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Data Center Team Lead

Data Scientist

Data Manager – Oil, Gas, Remediation

Head of Data

Data Center Team Lead

Senior Data Scientist, Systems Performance

Data Strategy and Governance Manager

Data Manager, Oil, Gas, Remediation

Research Data Scientist I

Data Scientist

Data & Analytics Lead

Senior Data Scientist