Data Scientist

• Lead the design and implementation of complex data science solutions to drive business impact and inform strategic decision-making
• Develop, validate, and optimize advanced statistical and machine learning models, including regression, classification, and forecasting algorithms
• Collaborate with cross-functional teams to translate business objectives into actionable analytics projects and deliver measurable outcomes
• Mentor and guide junior data scientists, fostering a culture of technical excellence and continuous learning
• Leverage Python, R, and relevant frameworks to build scalable data pipelines and automate model deployment using tools such as KubeFlow and BentoML
• Conduct rigorous statistical analysis, including hypothesis testing, T-Test, Z-Test, and probabilistic graph modeling to uncover actionable insights
• Implement and monitor model validation, explainability, and performance tracking using tools like Great Expectation and Evidently AI
• Stay current with emerging trends in machine learning, artificial intelligence, and big data technologies to drive innovation within the team

Senior Data Science Lead

• Partner with multiple management teams to ensure appropriate integration of data science functions to meet goals.
• Identify and define necessary system enhancements to deploy new data products and process improvements, with a focus on leveraging machine learning.
• Resolve a variety of high-impact problems and projects through in-depth evaluation of complex business processes, system processes, and industry standards, applying advanced statistical analysis and machine learning modeling techniques.
• Provide subject matter expertise in data science and advanced knowledge of AI/ML applications.
• Ensure application design adheres to the overall architecture blueprint.
• Develop complex machine learning models using the Python AI/ML stack.
• Design and implement solutions for anomaly detection, noun entity recognition (NER), and rule-based compliance policies.
• Build and deploy Agentic AI solutions using Large Language Models (LLMs).
• Utilize advanced knowledge of the machine learning model lifecycle to develop and enforce standards for coding, testing, debugging, and implementation of scalable, production-grade models.
• Develop a comprehensive knowledge of how areas of business integrate to accomplish business goals and to ensure the effective deployment of data science capabilities.
• Provide in-depth analysis with interpretive thinking to define complex business issues and develop innovative, data-driven solutions and machine learning models.
• Serve as an advisor or coach to mid-level data scientists and analysts, allocating work as necessary and fostering a culture of technical excellence.
• Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients, and assets.

Senior Data Scientist – Vice President

• Lead implementation of fraud risk strategies for consumer and commercial card businesses with decision engine systems in accordance with defined change control procedures and controls.
• Own and manage fraud rules, scores, and detection strategies, risk appetite execution, POS interdiction strategies and defect analysis.
• Lead gathering of business requirements, testing, and implementation of projects impacting fraud decision systems as well as system incidents.
• Leverage analytics to identify enhancement opportunities and more granular insights that can be acted upon, while ensuring adherence to Fraud Policy.
• Build effective relationships within and outside the Fraud organization to help ensure successful and timely execution of key portfolio priorities.
• Collaborate with cross-functional teams to provide strategy recommendations based on data and trend analysis and implement mitigation strategies.
• Continuously improve processes and strategies by exploring and evaluating new data sources, tools, and capabilities
• Leverage customer data to build risk segmentation/ mitigation strategies and complete complex analyses to identify strategy and procedure gaps, manage implementation process across several systems to affect change.
• Lead key analytical projects within the card first party fraud analytics team and support the Card lines of business by utilizing advanced predictive analytical and statistical techniques.
• Translate data into consumer or customer behavioral insights to drive targeting and segmentation strategies, and communicate clearly and effectively to business partners and senior leaders all findings
• Work closely with internal and external business partners in building, implementing, tracking, and improving fraud capture decision strategies.
• Refine framework for change control risk assessment including evaluation of control gaps within existing processes.
• Supports Governance process, evaluating historical performance and emerging changes in the environment.
• Prioritize and provide a clear line of sight to the most critical work to partners and team members.
• Mentor and coach junior team members.

VP Fraud Risk Business Analytics Lead

• You are the ideal candidate if you have experience managing large scale, complex programs and projects related to business process and system transformation, ideally ERP implementation using SAP’s S4 HANA solution.
• The Functional Data Lead Analyst will coordinate conversion activities, translate and document conversion requirements and detailed plans.
• They will drive execution tasks with various team members and stakeholders, track progress, and escalate issues.
• The Functional Data Lead Analyst will be expected to follow project management processes and best practices to ensure all tasks, activities, and resources are aligned to meet the overall program and organizational goals.
• Work cross functional to complete Build Phase, support system configuration design, testing, migration and hyper-care activities
• Completion of conversion functional specification documents, data mapping, cross reference documents and data model documents across multiple deployments
• Drive conversion progress, including task completion, milestone achievement to adhere to overall timeline.
• Lead meetings with cross functional thread leads
• Understanding team member workload, potential resource constraints, and ensure optimal resource utilization throughout the conversion process.
• Proactive risk management: identify, assess, mitigate, and monitor potential risks with real-time alerts for critical issues.
• Monitor and track conversion and deviation issue resolution.
• Communicate expectations and instill accountability in team members
• Resolve conflicts, promote work sharing, and motivate teams toward common goals.
• Manage multiple conversion activities, tasks and resources through effective organization prioritization, and time management practices to meet program objectives.
• Additional responsibilities include documentation, profiling analytics, reporting, internal stakeholder communication, and identifying areas for improvement to enhance quality and efficiency of cutover activities.

ERP Functional Data Lead Analyst

• 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

• Lead, coach, and grow a centralized team of data scientists working across pricing, forecasting, demand modeling, and broader predictive analytics—setting standards for technical rigor, code quality, and business impact.
• Own the modeling roadmap for the ML team, partnering with business and platform leaders to prioritize use cases, scope deliverables, and align modeling investments to measurable enterprise outcomes.
• Drive technical depth across the team—reviewing experiment design, feature engineering, model selection, validation strategy, and post-deployment monitoring with the rigor expected of a hands-on senior practitioner.
• Partner with AI Engineering, AI Product, and Data Engineering counterparts to ensure models are productionized on a modern MLOps stack with proper version control, retraining, and observability.
• Translate complex modeling work for executive audiences—framing tradeoffs, expected impact, confidence levels, and risks in language that supports clear decision-making.
• Build durable cross-functional partnerships with Pricing, Sales Operations, Supply Chain, Finance, and Corporate Analytics to ensure models are adopted, trusted, and tied to measurable business outcomes.
• Establish team operating cadence including planning, retros, model reviews, and documentation standards that scale as the team and portfolio of models grow.
• Recruit, develop, and retain top data science talent—owning hiring loops, leveling decisions, performance management, and career progression for direct reports.

Senior Manager, Data Science

• Designing and implementing state of the art Large Language Model (LLM) based agents that seamlessly synthesize complex information and initiate important actions in a business workflow.
• Using advanced Generative AI techniques deriving actionable insights from unstructured text data, such as call transcripts and emails.
• Predicting client interest basis their digital footprint and making relevant recommendations to drive higher client value delivery
• Leverage statistical and machine learning techniques to extract actionable insights from client retention data.
• Develop customer churn prediction models that proactively identify at-risk clients.
• Build tools to process structured and unstructured data.
• Engineering features and signals to train ML model from diverse data collection

• Lead a team of business data analysts responsible for implementation, enhancement, and maintenance of AML Transaction Monitoring platform
• Bring strong domain expertise in Securities Compliance and understand transaction patterns in trading
• Collaborate with Technology, Data, and Modeling teams for end-to-end delivery
• Ensure engagement with Audit and Compliance stakeholders to support issue validation

AML Business Data Manager

• Collect data and use data science techniques to uncover insights
• Provide oversight and consultative advice as the lead Data Scientist
• Evaluate technical solutions and lead innovation to solve complex business issues
• Provide strategic and day-to-day direction on CTR program activities
• Coordinate with internal and external stakeholders for audits and examinations

Lead Data Scientist

• 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 enterprise strategy for clinical data science, including predictive modeling, machine learning innovation, and causal evaluation of clinical programs.
• Develop and deploy advanced models that identify, stratify, and prioritize members for clinical and care management interventions.
• Oversee development of models for high-risk and rising-risk identification, avoidable utilization, chronic condition progression, and care management prioritization.
• Advance the organization’s AI capabilities by applying modern machine learning approaches, including deep learning and transformer-based models, to large healthcare datasets.
• Partner with clinical, operational, actuarial, and analytics leaders to translate business and clinical priorities into scalable data science solutions.
• Establish standards for model governance, monitoring, and performance management to ensure regulatory compliance, fairness, and responsible AI practices.
• Build, lead, and mentor a high-performing team of data scientists.

Senior Director, Clinical Data Science

• Own the Research Agenda
• Turn the Identity Graph Into Intelligence
• Bridge the Outside World to Internal Models
• Tell Stories That Change Minds
• Build & Lead a World-Class Team
• Influence Product, Models & Strategy
• Represent Socure Externally
• Drive Go-to-Market Differentiation

Head of Data Science – Fraud Insights

• Designing and implementing state of the art Large Language Model (LLM) based agents
• Using advanced Generative AI techniques deriving actionable insights from unstructured text data
• Predicting client interest basis their digital footprint
• Leverage statistical and machine learning techniques to extract actionable insights from client retention data
• Develop customer churn prediction models
• Build tools to process structured and unstructured data

• 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.

Data Scientist I

• Provide independent data science, machine learning, and analytical insights using member, financial, and organizational data to support mission critical decision making for various areas of the organization.
• Understand business needs and identify opportunities for new products, services, and process optimization to meet business objectives through the use of cutting-edge data science.
• Create descriptive, predictive, and prescriptive models and insights to drive impact across the organization.
• Conduct work assignments of increasing complexity, under moderate supervision with some latitude for independent judgment.

Senior Data Scientist, Credit Risk Modeling

• Actionable Insight: Generate compelling and actionable insights from complex, multi-source marketing data sets that directly inform channel investment, campaign design, and pipeline strategy.
• Stakeholder engagement: Establish strong collaborative relationships with marketing leaders and operators across demand gen, partner, field, product marketing, and brand, delivering high-impact analytics initiatives that translate loose, evolving requirements into clear deliverables.
• Attribution & Measurement: Design, build, and maintain the attribution framework for UpGuard – spanning first and multi-touch attribution and incrementality testing – and clearly communicate the trade-offs and assumptions behind each lens.
• Channel & Program Analytics: Develop a deep, first-principles understanding of channel logic across paid media, organic, content, lifecycle, partner, and field, and build the metrics, models, and dashboards that let each program owner self-serve their performance.
• Data Products: Partner with the data engineering team to design, construct, and maintain foundational marketing data assets – translating loose marketing requirements into well-specified dbt models that the whole organisation can rely on.
• Business Intelligence: Partner strategically with marketing stakeholders to provide robust self-service and conversational analytics capabilities, using design thinking principles to build user-friendly dashboards for funnel performance, channel ROI, partner sourced pipeline, and field event attribution.
• Deep Dive analysis: Personally conduct thorough, hands-on, technical analysis to diagnose and solve the most significant marketing challenges – from channel saturation and diminishing returns to lead quality decay and campaign cannibalisation.
• Commercial Analysis: Provide ongoing operational support to the commercial growth of the organisation, connecting marketing investment to pipeline, ARR, and payback in ways that finance and the executive team trust.

Marketing Analytics Lead

• The Director, Data Science & AI Engineering will lead the development and execution of the Firm’s enterprise data science, analytics, and AI engineering strategy.
• This role is responsible for building and managing a multidisciplinary team of Data Scientists, Data Analysts, and MLOps / AI Engineers focused on delivering innovative, production-ready AI solutions that support the Firm’s legal and business operations.
• Key responsibilities include overseeing the design, implementation, and optimization of LLM-powered applications, retrieval-augmented generation (RAG) systems, AI agents, custom models, advanced analytics, and business intelligence platforms that provide actionable matter, financial, and operational insights to firm leadership.
• This position serves as a strategic and hands-on leadership role within a rapidly evolving AI environment, responsible for establishing scalable architecture, governance, evaluation standards, security practices, and operational frameworks appropriate for a highly regulated professional services organization.

Director, Data Science – AI Engineering

• Design, develop, and deploy machine learning models, including LLMs, transformer-based models, and traditional ML approaches
• Build and optimize NLP, recommendation systems, and predictive analytics solutions to address complex business problems
• Develop and implement RAG (Retrieval-Augmented Generation), prompt engineering strategies, and emerging agentic AI workflows
• Conduct rigorous model evaluation using appropriate statistical methods and performance metrics
• Acquire, integrate, and preprocess structured and unstructured data from diverse sources (e.g., relational databases, NoSQL systems, logs, external datasets)
• Perform feature engineering and data preparation to ensure high-quality inputs for ML models
• Ensure data quality, validation, and governance compliance for AI/ML use cases
• Design and maintain scalable data and ML pipelines to support model training, evaluation, deployment, and monitoring
• Deploy models into production using cloud-native architectures and APIs
• Implement model monitoring, drift detection, and retraining strategies to ensure sustained performance
• Contribute to CI/CD pipelines and ML lifecycle automation
• Contribute to system architecture decisions across data, ML, and cloud platforms
• Provide technical leadership in selecting tools, frameworks, and design patterns for AI/ML solutions
• Mentor junior team members and promote best practices in data science and ML engineering
• Partner with stakeholders, product owners, and subject matter experts to translate business needs into AI/ML solutions
• Communicate findings, insights, and recommendations clearly to both technical and non-technical audiences
• Deliver solutions that drive measurable improvements in efficiency, quality, and business outcomes.

• Coordinates with government customer to determine process improvements for software Configuration Management.
• Performs planning, analyzing, and evaluating the effectiveness of software Configuration Management methods.
• Ensures supported programs follow Configuration Management security controls.
• Utilizes advanced qualitative and quantitative techniques to analyze and measure the effectiveness, efficiency, and productivity of organizational programs, establishing study methods and techniques and analyzing and evaluating the effectiveness of software running government applications.
• Participates in meetings and projects with stakeholders, staff, project managers, etc. to render project activities into actionable solutions across business units and initiatives.
• Effectively assesses and responds to highly complex and time sensitive issues.
• Reviews business requirements and other documentation from multiple sources to identify and resolve cross initiative dependencies and ensure consistency of configuration management approach and application across the government program offices.
• Provides guidance or technical supervision to less experienced staff performing configuration management functions.
• Provides Configuration Management software system tool administrative control, to include account creation, setting roles/permissions, providing system training, and troubleshooting issues.
• Reviews program Systems Engineering Plans in support of Technical Reviews to ensure configuration management requirements are adhered to.
• CM Analyst validates the deliverables depicted in the SEP to ensure the artifacts are reposed in the CM repository in support of the specific Systems Engineering Technical Review (SETR) event.

Data Science Specialist, Configuration Analyst 3

• Design and develop statistical, machine learning, forecasting, and optimization models
• Apply techniques such as regression, classification, clustering, time series, and anomaly detection
• Translate business problems into scalable analytical approaches and measurable outcomes
• Evaluate model performance, accuracy, stability, and business impact
• Lead models from concept through production deployment and ongoing optimization
• Partner with engineering teams to operationalize models into applications and workflows
• Define and support model lifecycle processes, including versioning, monitoring, and retraining
• Build reusable, maintainable, and well-documented modeling pipelines
• Monitor model performance, drift, and usage; troubleshoot production issues as needed
• Collaborate with stakeholders to define objectives, constraints, and success metrics
• Identify and prioritize high-value data science opportunities
• Communicate results, assumptions, risks, and recommendations clearly to technical and non-technical audiences
• Support adoption by ensuring outputs are actionable, interpretable, and aligned to business needs
• Perform exploratory data analysis to identify patterns and opportunities
• Assess data quality, completeness, and suitability for modeling
• Design and validate features that improve model performance
• Partner with data teams to enhance analytical datasets and reusable data products
• Apply and promote best practices for reproducibility, model governance, and responsible AI
• Ensure alignment with enterprise standards for security, privacy, and compliance
• Mentor data scientists and analysts on modeling techniques and production readiness
• Lead technical and model reviews and contribute to data science standards and frameworks

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Data & Analytics Lead

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Data Scientist I

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