Data Scientist

• Use statistical analysis, machine learning methods, and advanced data technologies to analyze complex datasets and develop predictive models
• Leverage programming languages such as SAS, SQL, SPSS, R, Python to manage, extract, and analyze data from relational databases
• Collaborate with clinical investigators on human-based study design and statistical analysis
• Document best practices and solution frameworks
• Communicate and present analytical findings to other investigators

Research Data Scientist II – Quantitative Health Sciences

• Own and lead end-to-end execution of customer activation strategies, from requirements intake through deployment, QA, and post-launch validation.
• Design, build, and maintain end-to-end journey-based campaigns, including audience segmentation, triggers, decisioning, and orchestration.
• Translate marketing requirements into practical activation solutions within AEP and AJO
• Establish and enforce best practices for activation design, journey configuration, and execution standards.
• Partner with Analytics and business stakeholders to ensure campaigns deliver measurable outcomes
• Identify optimization opportunities to improve campaign performance, scalability, and operational efficiency.
• Provide day-to-day leadership, prioritization, and oversight for activation work across the team
• Support and maintain data pipelines and datasets within AEP/RTCDP to enable activation use cases
• Work with XDM schemas, audiences, and profiles to ensure data is structured and usable for segmentation and personalization
• Troubleshoot data quality, audience qualification, and activation issues
• Ensure adherence to data governance, consent, and privacy standards

Senior Marketing Activation, Data Lead

• 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

• Partner with senior leadership to provide the financial & analytical perspective in the development of the company’s strategy
• Lead the effort required to model and iterate the results of the strategic planning objectives using internal production projections in conjunction with anticipated market rates and conditions
• Drive the efforts to develop forecasting capabilities as output of our balance sheet modeling and analytics in partnership with senior leaders across the organization
• Participate as a key leader in the company’s ALCO and Investment Committees, having the ability to interpret results and identify possible strategies to address risk management concerns
• Provide analysis and interpretation of quarterly results to Investor Relations Manager and Chief Financial Officer to provide insight relevant to possible investor and analyst demands
• Manage cross-functional relationships and participate in teams as a representative of Finance providing a perspective on our strategic goals with the ability to initiate and/or adapt to and communicate adjustments made in reaction to market and industry changes

Financial Planning, Forecasting, and Analytics Leader

• This Data Science Manager Senior role focuses on delivering decision ready insight through Descriptive (“what happened?”) and Diagnostic (“why did it happen?”) Analytics, with concrete outputs such as executive dashboards, KPI frameworks, trend and cohort analyses, drill downs, segmentation, driver analysis, and root cause investigations.
• The role is accountable for translating complex data into clear performance narratives by defining standardized metrics, ensuring data quality and lineage, and embedding analyses into scalable BI assets rather than ad hoc reporting.
• As needed, the role performs Prescriptive Analytics (“what should we do?”) by turning findings into decision recommendations through scenario analysis, sensitivity testing, and practical optimization or decision models, and by operationalizing insights into dashboards, alerts, and playbooks.
• Predictive Analytics (“what will happen?”) is in scope but not required, with forecasting, risk or propensity scoring, and applied machine learning viewed as nice to have capabilities.
• The manager leads and prioritizes an analytics roadmap, coaches analysts and data scientists, and promotes disciplined use of tools such as SQL, Python, modern BI platforms, cloud data technologies, Git, and applied statistics.
• Strong partnership with product, operations, risk, and business leaders is essential to frame the right questions and deliver executive ready storytelling that connects analysis to action.
• The role enforces data governance, documentation, and audit ready practices, ensuring metric consistency and trusted reporting across teams.
• An experiment and measurement mindset is expected, applying test and learn or A/B testing to evaluate impact. Any models or advanced techniques used are monitored for performance, reliability, and fairness, with assumptions and limitations communicated transparently.

Senior Data Science Manager – Data, Modeling & 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

• 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

• Use analytical skills and data science knowledge to create real-world impact
• Work closely with clients to understand their questions and needs
• Dig into their data-rich environments to find pieces of their information puzzle
• Develop algorithms and systems
• Use the right combination of tools and frameworks to turn sets of disparate data points into objective answers
• Provide a deep understanding of the data, meaning, and how it can be used

• 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 complex data science and machine learning initiatives supporting supply chain, manufacturing operations, capacity planning, demand forecasting, and operational decision-making
• Design, develop, and own advanced ML solutions — including predictive models, time-series forecasting, optimization, and decision-support systems
• Build, train, evaluate, and interpret machine learning models to quantify supply chain drivers, surface optimization opportunities, and improve operational outcomes
• Develop and operationalize analytics and ML solutions using Databricks for large-scale data processing, model development, and experimentation
• Design and build multi-agent AI systems using frameworks such as Azure AI Foundry, AutoGen, Semantic Kernel, or LangChain/LangGraph
• Implement and extend solutions using the MCP
• Apply data science best practices including feature engineering, model validation, performance monitoring, reproducibility, and documentation
• Partner with Supply Chain & Procurement leadership, Manufacturing Ops, Process Engineering, Demand Planning, and IT
• Develop and maintain self-service, automated, and AI-enabled analytics workflows
• Leverage Azure AI Foundry, Microsoft Copilot Studio, and Microsoft 365 Copilot extensibility to prototype and deploy AI-powered analytics tools
• Produce executive-ready insights through clear storytelling using Power BI or embedded analytics
• Set technical direction, establish reusable ML and AI frameworks, and mentor junior and mid-level data scientists.

Lead Data Scientist

• Partner with multiple stakeholders to size out and align on strategy for upcoming marketing campaigns
• Evaluate key marketing levers for future optimization
• Develop a learning agenda for the continuous refinement and optimization of marketing campaigns, utilizing statistical techniques and experimental design
• Support the monthly forecasting process, estimating future performance based on historical data
• Ensure adherence to compliance and legal regulations and policies on all projects managed
• Strategically engage with all levels of professionals and managers across the enterprise and serve as an expert advisor to senior leadership

Senior Test Design and Campaign Planning Consultant, Data Science

• Provide end to end governance and controls oversight for Finance Technology and ALM processes, ensuring SOX/ICFR, regulatory reporting, and ALM controls are designed, operating, and sustained as systems evolve.
• Act as a strategic advisor to Finance, Technology, Risk, and Audit leaders, translating regulatory and control expectations into practical, technology enabled solutions.
• Embed controls into technology change, enhancements, conversions, and decommissioning to prevent downstream audit and regulatory issues.
• Develop and leverage analytics and dashboards to monitor control health, data quality, and process compliance, shifting from point in time testing to proactive monitoring.
• Drive issue management and remediation, ensuring root causes are addressed, risks are clearly communicated, and the organization remains audit and regulator ready.
• Leads the analytics processes across multiple functions or business units leveraging an array of complex analytical tools to create data driven solutions.
• Serves as mentor to more junior employees.
• Consults with clients and mentors consultants on tool and strategy implementation and monitoring, statistical scoring, business intelligence, data quality, and analytical product / solution development.
• Determining the optimal analytic approach and supporting development, implementation and enhancements.
• Assists peers with guidance on analytic approaches related to more complex solutions.
• Conceptualizing, developing and continuously optimizing analytical solutions for business leadership to enable data driven decision making.
• Analyze results and make recommendations for key business partners and senior management or communicate conclusions from complex analytical solutions to a wide range of audiences.

Business Analytics Lead – Finance, Accounting, Technology

• Work from anywhere as a Senior Revenue Cycle Analytics & Data Science Expert.
• You will leverage complex data to guide high-level business decisions.
• Your focus will be on leveraging data science, including predictive and prescriptive analytics.
• Develop, test, and deploy standard reports and certified datasets for business leaders.

Senior Revenue Cycle Analytics, Data Science Expert

• Develop AI solutions: Create ML and generative AI systems for RAG pipelines, chatbots, classification, forecasting, and recommendation.
• Ensure alignment with enterprise standards, seamless integration, and secure scalability.
• End ‑ to ‑ End Solution Development: Own the AI lifecycle from problem framing through deployment: data prep, modeling, evaluation, model change management, orchestration , observability , drift detection, and synthetic data generation.
• Collaborate closely with AI engineers, data engineers, platform, security, and IT to ensure solutions are robust, maintainable , production ready, follow safety filters/guardrails, and rollback plans.
• Drive execution from discovery to rollout by defining scope, milestones, and acceptance criteria; managing dependencies/risks; coordinating cross-functional workstreams; and maintaining clear status reporting, issue escalation, and delivery timelines.
• Partners closely with Product, Underwriting, Distribution, Risk, Legal, and Compliance to align AI initiatives with enterprise objectives and governance expectations.
• Translates complex model behavior and evaluation outcomes into clear, actionable business insights with defined success criteria (accuracy, cost, performance, reuse, ROI).

Senior Applied AI Data Scientist

• Build and optimize ML, deep learning, and GenAI models using modern frameworks (PyTorch, TensorFlow, scikit-learn)
• Design and deliver RAG pipelines, hybrid retrieval systems, and vector-based search workflows
• Develop Agentic AI solutions including tool orchestration, reasoning flows, and safe-execution strategies
• Run structured experimentation using evaluation metrics (BERTScore, BLEURT, semantic similarity, retrieval precision/recall)
• Integrate solutions into GCP (Vertex AI, Workbench, Vector Search) and AWS (SageMaker, Bedrock)
• Build ingestion, enrichment, and semantic retrieval flows for high-quality knowledge and feature engineering
• Partner with onshore and offshore DS/ML engineers to ensure quality, consistency, and shared technical patterns

Applied AI Data Scientist

• Gather, preprocess, and validate datasets from internal and external sources.
• Identify trends, patterns, and anomalies using statistical and visual techniques.
• Build and evaluate predictive models using machine learning algorithms.
• Create dashboards and visual reports to communicate findings to stakeholders.
• Maintain clear records of methodologies, code, and project outcomes.
• Work closely with cross-functional teams to align data solutions with business needs.

Data Science, AI Intern

• Collaborate with leaders across the organization to develop and execute on a data strategy inline with ActBlue's near and long term goals, including supporting organizational growth through acquisitions and partnerships
• Foster a data centric culture by upleveling our ability to make data-informed decisions and build best in class data products that meet rigorous performance and reliability standards
• Enable and advocate for AI/ML driven products and features across the ActBlue Platform through building scalable machine learning infrastructure that serves production environments and identifying new opportunities to deploy data science and analytics capacity in service of our mission
• Oversee our Data Science, Data Analytics, Data Engineering, and Data Platform teams in a fast-paced, mission-driven environment
• Guide the evolution of our data systems, tools, and architecture to meet the organization's needs, including data consolidation, governance frameworks, and unified data models across multiple entities
• Ensure high-quality communication and a stakeholder-centric approach between our data teams and their internal customers, with an emphasis on responsiveness and partnership during high-stakes moments

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