Data Scientist

• Provide technical leadership across the Analytics function
• Create, develop, and maintain AML typologies
• Collaborate with cross-functional teams and support Financial Intelligence Units (FIU)
• Oversee the execution of multiple projects
• Ensure successful project completion within established timelines

Data Scientist – FCRM

• Develop and interpret information that supports executive decision-making, policy formulation, growth projections, and portfolio oversight, with a strong focus on technology investments and operational performance.
• Own the end‑to‑end reporting and analytics lifecycle, including data sourcing, pipelines, models, dashboards, and access controls, to ensure stable, reliable, and consistent executive reporting outputs.
• Analyze data from multiple sources to produce a comprehensive view of business, financial, and operational performance on weekly/monthly/quarterly cadences.
• Design, create, and deliver executive‑ready reports, dashboards, and presentations, translating complex data into clear narratives and actionable insights for senior leadership and the Board.
• Develop recommendations and prepare written and oral presentations that synthesize insights into decisions, highlighting trends, risks, opportunities, and required actions.
• Evaluate investment performance and ROI, track progress against strategic objectives and forecasts, and identify root causes behind variances and emerging trends.
• Implement and maintain data, governance, hygiene and quality processes; proactively monitor accuracy, consistency, and reliability of reporting, and drive continuous improvement.
• Develop performance measurement framework and analytical tools to improve operational efficiency and maximize value realization from technology and operational investments.
• Coordinate revenue and budgeting processes; establish and maintain high standards for data quality, efficiency, and consistency, and reporting cadence.
• Establish effective analysis of business trends and end-to-end processes to identify significant variances and underlying driver, recommend corrective actions to management.
• Collaborate with business partners and cross-functional teams to define reporting standards, best practices and scalable standard operating procedures.
• Continuously assess resource utilization and capacity and make data-driven recommendations to ensure an optimal and sustainable support model.
• Lead analytical and reporting initiatives from concept through execution, coordinating stakeholders and driving measurable outcomes with a proactive, execution‑oriented mindset.

Technology Portfolio Reporting & Analytics Lead

• Analyzing and interpreting large datasets to uncover potential revenue generation opportunities
• Collaborating with key stakeholders to comprehend business problems
• Utilizing data gathering and analysis techniques to devise solutions
• Presenting recommendations based on the findings
• Performing business analytics, which include data analysis, trend identification, and pattern recognition
• Applying agile practices for project management
• Creating and maintaining technical documentation
• Managing multiple priorities and maintaining quality and timeliness of work deliverables
• Delivering engaging presentations and effectively communicating technical concepts
• Mitigating risk by identifying potential issues and developing controls
• Researching the latest advances in the fields of data science and artificial intelligence

Data Scientist I

• Own clinical data quality across claims, EHR, and ADT: Define standards for how clinical data is structured, normalized, and validated as modeling inputs across payer claims (medical, pharmacy, eligibility), EHR data (Epic, Cerner, Athena), and real-time ADT feeds. Bring deep familiarity with EHR data formats (FHIR, HL7, C-CDA) and how data from systems like Epic, Cerner, and Athena maps to clinical reality. Hold the bar for clinical accuracy and completeness across all three sources.
• Build and ship production ML/AI models: Develop, validate, and deploy risk stratification, care gap prediction, treatment effect estimation, and LLM/foundation model applications — with rigor around leakage, calibration, fairness, and clinical face validity.
• Apply health economics and outcomes methods: Translate raw clinical and claims data into decision-grade evidence through risk adjustment, utilization measurement, cost attribution, quasi-experimental evaluation, and outcomes measurement aligned with CMS, NCQA, and MCO reporting standards.
• Advance machine and AI products: Bring senior modeling judgment to the product roadmap, owning the clinical and methodological soundness of what ships.
• Set standards and mentor: Make architectural trade-offs, drive alignment across data science, engineering, product, and clinical stakeholders, and mentor junior data scientists to raise the technical bar of the team.

Principal Data Scientist, Health Informatics

• Designing and implementing AI systems to transform raw data into actionable insights
• Leading teams in the development of scalable machine learning models and solutions
• Managing complex data analysis and integration to support AI-driven initiatives
• Utilizing programming languages such as Python and Java to enhance AI model deployment
• Overseeing the creation and maintenance of data pipelines and infrastructure
• Applying deep learning techniques and neural networks to improve predictive analytics
• Collaborating with stakeholders to address data challenges and optimize AI applications
• Mentoring team members to develop skills in AI implementation and data engineering
• Validating data quality and compliance within AI frameworks
• Encouraging innovation and embracing change to drive business growth through AI solutions

AI & GenAI Data Scientist – Manager

• Designing and implementing AI systems to transform raw data into actionable insights
• Developing and deploying scalable AI and Machine Learning solutions using advanced technologies
• Collaborating with clients to understand their data needs and deliver tailored solutions
• Utilizing programming languages such as Python and C++ to build robust data models
• Managing data pipelines and confirming data quality and integration across platforms
• Applying machine learning libraries like TensorFlow and Scikit-Learn to enhance model performance
• Conducting complex data analysis to inform strategic decision-making
• Leveraging natural language processing and text analytics for innovative AI applications
• Building and maintaining data infrastructure to support AI-driven automation
• Mentoring junior team members and fostering a collaborative work environment

Senior Associate, AI & GenAI Data Scientist

• Lead the end‑to‑end enterprise portfolio operations function.
• Establish and maintain portfolio governance frameworks, scorecards, and KPIs.
• Champion enterprise data transformation strategy.
• Analyze and optimize cross‑functional processes, workflows, and operating models.
• Serve as a trusted advisor to senior leadership.
• Assess organizational structure, roles, and capabilities; recommend improvements.

Head of Data Transformation – Portfolio Operations

• Support data science and analytics projects across the full lifecycle, including data gathering, cleaning, and exploratory analysis
• Assist in developing and testing predictive or descriptive models under the guidance of senior team members
• Analyze data sets to identify trends, patterns, and actionable insights
• Build dashboards and visualizations to communicate findings to technical and non-technical stakeholders
• Partner with cross-functional teams (engineering, product, business teams) to understand requirements and translate them into analytical approaches
• Contribute to documentation of data workflows, methodologies, and results
• Perform testing, validation, and quality checks on data and analytical outputs
• Present findings and recommendations to team members and leadership (Aligned with existing expectations such as collaborating with development teams, analyzing results, and presenting findings to meet business needs)

Data Science, Analytics Intern – Technology Solutions

• 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

• Partner with Marketing to Define and Solve Problems – Work closely with marketing stakeholders to understand business challenges, define success metrics, and translate needs into analytical approaches that drive performance across campaigns and channels.
• Design and Deliver Data-Driven Solutions – Apply statistical analysis and machine learning to develop solutions that address business needs, then present findings, influence decisions, and gain alignment on adoption.
• Lead Experimentation and Optimization – Develop and manage testing frameworks (A/B testing, campaign experimentation) across channels and markets. Analyze results and provide clear recommendations to improve performance and inform future strategy.
• Translate Results into Business Impact – Clearly communicate insights and quantify outcomes (e.g., campaign performance lift, engagement improvements, ROI) to ensure stakeholders understand the value and take action.
• Partner to Operationalize Solutions – Collaborate with Technology and Engineering teams to transition validated models into production, supporting implementation through scalable pipelines and processes.
• Influence Through Storytelling – Present insights and recommendations to both technical and non-technical audiences, including senior stakeholders, to drive alignment and decision-making.

Senior Data Scientist – Marketing Analytics

• Develop, test, validate, and maintain a portfolio of rating models for the Employee Benefits class plans in Long-Term Disability, Short-Term Disability, and Life
• Continuously partner with Actuarial and Data teams to monitor and manage the End-to-End lifecycle of the rating models and underlying data which feeds them
• Lead cross-functional projects that include the creation of statistical models and machine learning techniques to achieve financial objectives, solve business problems, and identify long-term opportunities that enhance actuarial modeling.
• Collaborate and partner with business stakeholders in a way that supports the vision and sustains a culture that treats analytics as a corporate asset.
• Advance the department’s capabilities by creating and deploying long-term tools to continually evolve the practice of data science, with an ability to see the end-to-end solution.
• Develop strategies to achieve targeted business objectives.
• Implement these strategies and follow through to successful conclusion.
• Remain current on research techniques and become familiar with state-of-the-art tools applicable to your function.
• Participate in the talent management process for hiring, onboarding, training and development of staff.
• Collaborate with your leader to provide timely feedback on development and opportunities for your team.
• Learn/bring best practices to guide the direction of our Data Science and Data Engineering workflows.

Director of Data Science, Actuarial Modeling

• Support Wholesale Banking’s enhanced data and analytics strategy
• Partner closely with a Business Enablement Lead
• Serve as a data expert
• Consult on business needs and priorities
• Create requirements and help track and prioritize analytics pipeline
• Manage a team for rapid response outputs and ad-hoc reporting
• Track progress of reporting deliverables
• Partner on change management/adoption approaches
• Advance analytic capabilities
• Lead team in design and production of business reporting/competitive intelligence
• Develop and maintain strong working relationships
• Research, analyze, design data sources in support of projects
• Execute data and report validations
• Drive efficiencies and automation in reporting and analytic work

LOB Analytics Lead – Wealth

• 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

• Develop end-to-end Generative AI solutions leveraging LLMs and foundational AI components
• Design and implement scalable RAG (Retrieval-Augmented Generation) workflows
• Develops complex queries and performs extensive programming to access, transform, and prepare data for statistical modeling.
• Leads and executes deep dive diagnostic, predictive, and prescriptive analytics to support data-driven business decision making.
• Mentors and develops junior data scientists and analysts.
• Identifies and diagnoses data inconsistencies and errors, documents data assumptions, and forages to fill data gaps.
• Engages with internal stakeholders to understand and probe business processes in order to develop hypotheses.
• Brings structure to requests and translates requirements into an analytic approach.
• Guides test design, research design, and model validation.
• Provides statistical consultation services.
• Serves as the analytics expert on cross functional teams for large strategic initiatives and contributes to the growth of the Vanguard analytic community.
• Prepares and delivers insight presentations and action recommendations.
• Communicates complex analytical findings and implications to business leaders.
• Participates in special projects and performs other duties as assigned.
• Designs scalable data mining models and reporting protocols to identify trends and patterns in massive datasets.
• Applies modern and emerging data science techniques to solve business problems across functions such as marketing, economics, and operations.
• Contributes to the advancement of analytics through exploratory research and application of cutting-edge statistical theory.

Senior Data Scientist

• Translate complex business challenges into practical, data-driven solutions using statistical analysis, machine learning, simulation, and optimization techniques.
• Design and deliver scalable solutions that support internal decision-making and power customer-facing applications.
• Leverage expertise in databases, cloud platforms, and programming to develop advanced analytical models.
• Collaborate with peers to enhance internal tools and expand the organization’s library of data science capabilities.
• Gathers, interprets, and manipulates structured and unstructured data to enable advanced analytical solutions for the business.
• Develop scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value.
• Select the appropriate modeling technique and/or technology considering data limitations, application, and business needs.
• Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
• Communicate outcomes to non-technical business colleagues with focus on business action and recommendations.

• Gathers, interprets, and manipulates complex structured and unstructured data to enable advanced analytical solutions for the business.
• Leads and conducts advanced analytics leveraging machine learning, simulation, and optimization to deliver business insights and achieve business objectives.
• Guides team on selecting the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs.
• Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
• Composes and peer reviews technical documents for knowledge persistence, risk management, and technical review audiences.
• Partners with business leaders from across the organization to proactively identify business needs and proposes/recommends analytical and modeling projects to generate business value.
• Works with business and analytics leaders to prioritize analytics and highly complex modeling problems/research efforts.
• Leads efforts to build and maintain a robust library of reusable, production-quality algorithms and supporting code, to ensure model development and research efforts are transparent and based on the highest quality data.
• Assists team with translating business request(s) into specific analytical questions, executing analysis and/or modeling, and communicating outcomes to non-technical business colleagues with a focus on business action and recommendations.
• Manages project portfolio milestones, risks, and impediments.
• Anticipates potential issues that could limit project success or implementation and escalates as needed.
• Establishes and maintains best practices for engaging with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and model risk management standards.
• Interacts with internal and external peers and management to maintain expertise and awareness of cutting-edge techniques.
• Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies.
• Serves as a mentor to data scientists in modeling, analytics, computer science, business acumen, and other interpersonal skills.
• Participates in enterprise-level efforts to drive the maintenance and transformation of data science technologies and culture.
• Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.

Lead Data Scientist

• Apply diverse statistical and machine learning techniques to analyze a variety of datasets to solve complex, unstructured business problems.
• Actively contribute to our team’s vibrant culture through sharing knowledge, helping peers, and generally being a great person to work with.
• Develop creative technical solutions to improve the company’s decision making, the customer’s experience, and shareholder returns.
• Evaluate non-traditional data sources to see if they result in better underwriting decisions, saving our hard-working customers millions in interest payments.
• Mine the “digital exhaust” of our marketing campaigns to identify new populations who would benefit from our products.
• Partner with our operations teams to surface data insights that will allow our thousands of branch staff to better serve our customers.

Data Science Analyst

• Lead analytical support for portfolio strategy and enterprise initiatives
• Build and maintain advanced analytical models to support scenario development
• Analyze large, complex datasets to identify trends, key drivers, risks, and opportunities
• Develop clear metrics, scorecards, and business cases that support strategic and investment decisions
• Prepare high-quality Excel and PowerPoint deliverables that convey insights and support decision-making
• Provide ad hoc analysis for leadership requests, internal reviews, and strategic initiatives
• Manage multiple concurrent projects independently, prioritizing effectively and meeting deadlines
• Partner cross-functionally to drive action

Corporate Real Estate Portfolio Strategy Lead

• Architect, build, and lead our entire technology and data infrastructure
• Evaluate our existing Lightspeed POS data and design the data architecture from the ground up
• Architect a unified data model connecting in-store POS transactions, customer profiles, product catalogs, inventory, and future e-commerce and web analytics into a single source of truth
• Design and implement identity resolution to create a single golden customer record from fragmented data across all channels
• Build the integration layer between Lightspeed POS and the central data platform with near-real-time sync, monitoring, and alerting
• Establish data governance policies: quality standards, naming conventions, access controls, and documentation
• Lead the evaluation, selection, and build of our e-commerce platform and brand website from scratch
• Ensure the e-commerce platform integrates deeply with the data architecture from day one: shared customer profiles, unified inventory, consistent product data across channels
• Implement analytics and behavioral tracking to capture browsing and purchase data that feeds into AI models
• Build the digital foundation for personalized experiences: product recommendations, beauty profile-driven content, and intelligent search
• Design and build the AI Feature Store — pre-computed customer and product features powering recommendation engines, churn prediction, demand forecasting, and personalized marketing
• Develop the AI roadmap, identifying high-impact use cases: personalized recommendations, virtual consultations, intelligent inventory replenishment, and automated customer engagement
• Hire and manage a small IT team: Database/Integration Engineer, E-Commerce Developer, Data Analyst, and IT Support Technician
• Manage vendor relationships with Lightspeed, hosting providers, e-commerce partners, and contract consultants
• Define and enforce data security policies for customer data across all current and new systems
• Ensure PCI DSS compliance across retail locations and the new e-commerce platform

IT Manager, Data Manager

• Lead the design and delivery of advanced medical cost forecasting solutions that improve claim reserving and enterprise financial forecasting
• Prototype and validate new analytical approaches using techniques such as time series modeling, simulation, and machine learning
• Create diagnostics and performance measures to evaluate forecast accuracy and explain key cost drivers
• Partner with finance, actuarial, and business teams to translate forecasting needs into actionable analytics solutions
• Communicate findings through clear insights, presentations, and technical documentation to support decision-making
• Develop early-warning analytics to identify emerging cost trends and risks
• Collaborate with data engineering and product teams to ensure scalable and interpretable solutions
• Contribute to model monitoring, governance, and continuous improvement processes
• Mentor junior team members and share best practices across the data science community

See all jobs on JobTailor

Data Scientist – FCRM

Technology Portfolio Reporting & Analytics Lead

Data Scientist I

Principal Data Scientist, Health Informatics

AI & GenAI Data Scientist – Manager

Senior Associate, AI & GenAI Data Scientist

Head of Data Transformation – Portfolio Operations

Data Science, Analytics Intern – Technology Solutions

Data Scientist – Corporate & Institutional Banking

Senior Data Scientist – Marketing Analytics

Director of Data Science, Actuarial Modeling

LOB Analytics Lead – Wealth

Data & Analytics Lead

Senior Data Scientist

Data Scientist I

Lead Data Scientist

Data Science Analyst

Corporate Real Estate Portfolio Strategy Lead

IT Manager, Data Manager

Senior Advisor, Data Science – Medical Cost Trend & Forecasting