Data Scientist

• Lead the design, development, and implementation of advanced machine learning, generative AI solutions across structured and unstructured data
• Build and deploy scalable, automated data science products and reusable analytic assets that support business and client objectives
• Analyze large-scale, high-dimensional datasets using modern analytics tools and distributed computing platforms to identify trends, opportunities, and actionable insights
• Design and prototype agentic AI workflows leveraging large language models (LLMs), retrieval systems, structured data, APIs, tools, and business rules to automate complex business processes
• Translate business requirements into AI agent architectures, including task decomposition, tool orchestration, routing logic, escalation paths, and human approval checkpoints
• Optimize retrieval-augmented generation (RAG) solutions through embedding evaluation, metadata design, reranking approaches, citation quality assessment, and knowledge freshness validation
• Develop evaluation frameworks for machine learning and AI solutions, including performance, accuracy, reliability, hallucination risk, latency, cost, safety, and consistency measures
• Make key analytical and architectural decisions, establish technical standards, and provide leadership on methodology, model selection, solution design, and AI implementation best practices
• Lead code reviews, mentor data scientists, and promote best practices in software development, responsible AI, model governance, and analytics delivery
• Partner with engineering, platform, and business teams to deploy, monitor, and maintain production-ready analytics and AI solutions
• Translate complex analytical findings into clear reports, visualizations, and presentations for technical and non-technical audiences
• Collaborate with stakeholders and clients to define analytical approaches that address strategic, operational, and clinical objectives
• Evaluate emerging technologies and industry trends to advance organizational analytics and AI capabilities.

Lead Data Scientist

• Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
• Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
• Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals

Manager, Data Scientist – Partnerships Acquisitions

Principal Associate, Data Scientist – Partnerships Acquisitions

• Analyzing and interpreting large datasets to uncover potential revenue generation opportunities and develop effective risk management strategies
• Collaborating with key stakeholders to comprehend business problems
• Utilizing data gathering and analysis techniques to devise solutions
• Presenting recommendations based on the findings
• Developing advanced technical documentation for an array of models
• Working closely with developers to understand how the model works and ensure results are accurate
• Partnering with technology and model users to schedule deployments
• Conducting regular model monitoring and sharing performance results

Data Scientist I – Fraud Model Governance

• Build predictive models to optimize lending decisions by using advanced statistical modeling and simulation techniques
• Use advanced data analytics to understand consumer risk behavior trends, their impact on the portfolio, and resulting business opportunities
• Support the implementation of models by working closely with the software engineering team and performing post release checks and analysis
• Assist with development of technical tools and platforms to standardize and streamline model development and monitoring processes
• Establish key performance metrics and develop automated reporting dashboards using SQL and Python
• Design A/B testing for models and underwriting strategies, evaluate impact, and provide recommendations
• Work with cross-functional teams to explore new ways of finding insights from data and provide recommendations
• Communicate concise and actionable business strategies to senior management

• Lead the end-to-end design, development, and implementation of new and innovative rating plans for personal auto and home insurance products.
• Build and refine pricing models using GLMs, GBMs, and other ML techniques to sharpen risk segmentation and deliver competitive price accuracy.
• Drive feature engineering efforts and identify, evaluate, and integrate external data sources to enhance model performance and predictive power.
• Design and build efficient, scalable, and repeatable processes for model development, data pipelines, and rating plan deployment.
• Own the technical documentation, validation, and peer review of rating plan changes to ensure accuracy and compliance.
• Partner closely with Product, Data Science, Engineering, Underwriting, and other business and analytical teams to develop and implement changes.
• Mentor and provide technical guidance to junior actuaries and data scientists, fostering a collaborative and high performance team culture.

Lead Actuarial Data Scientist

• Partner with a cross-functional team of data scientists, software engineers, and product managers to identify and quantify risks associated with models
• Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
• Lead teams of data scientists who build machine learning models to challenge current champion models
• Lead validation of a wide variety of models across multiple business domains

Principal Associate, Data Science – Model Risk Office

Principal Associate, Data Scientist – Cash Flow Underwriting

• Collaborate with business partners including commercial insights and analytics, brand teams, sales operations, data teams, external vendor partners, media agencies, service owners, and IS partners to create solutions and insights to solve challenging business problems across multiple therapeutic areas and commercial functions.
• Apply deep technical expertise to manipulate large messy datasets, write high-quality code, and adapt complicated machine learning models to the problems at-hand.
• Transform business, medical, or scientific questions into analytical ones and mapping out solutions, delivery, and impact.
• Identify business needs and proposing potential analytical approaches for solutions.
• Build high-performance algorithms, prototypes, predictive models, and proof of concepts using Python or R with reliable performance, interpretability, and action-ability.
• Effectively communicate cross-functionally with stakeholders to develop appropriate methodology to answer specific business questions.
• Help further build the team, including by contributing ideas and standard methodologies and keeping abreast of developments in industry and academia.
• Create powerful stories in PowerPoint, with knowledge of MS Office Suite (Excel and PowerPoint).

CD&A Data Scientist

• Define Ads ontologies and metrics for campaigns, budgets, bids, creatives, audiences, and placements.
• Build dbt models and curated marts in Snowflake with clear data contracts, tests, and SLOs.
• Ingest and enrich unstructured Ads content and publish retrieval‑ready datasets using our managed search/vector services.
• Design and evaluate retrieval workflows (RAG) with existing services for hybrid search and re‑ranking; set quality/latency targets and iterate via experiments.
• Design agent reasoning and policies on ads, including tool definitions and human‑in‑the‑loop approvals.
• Establish evaluation suites covering precision/recall, calibration, hallucination rate, latency, and cost.
• Run A/B or uplift experiments to quantify impact and guide iteration.
• Translate Ads problems into agent behaviors and own KPIs such as ROAS lift, pacing accuracy, RCA precision/recall, forecast MAPE, and time‑to‑insight.

Ads AI Analytics Lead

• 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

• Partner with business leaders to define key performance metrics and measurable success criteria.
• Conduct exploratory analysis to identify revenue drivers, cost opportunities, and operational inefficiencies.
• Develop forecasting, segmentation, and predictive models to support planning and decision-making.
• Design and evaluate A/B tests and experiments to measure business impact.
• Translate analytical findings into clear executive-ready recommendations.
• Build and maintain scalable dashboards and KPI reporting using BI tools.
• Quantify financial impact and ROI of analytics initiatives.
• Present insights to senior leadership and influence strategic direction.

• Lead product and portfolio analytics for key USCC partnership card portfolios, providing performance insights, forward-looking trends, and strategic recommendations to increase acquisition volumes and accelerate growth.
• Build diagnostics frameworks and analytical insights to map underlying drivers, emerging risks and opportunities to drive growth.
• Design next generation KPIs that summarize and inform both expense and revenue opportunities.
• Drive efforts on ad hoc, time sensitive exploratory data analysis, confirmatory data analysis and/or qualitative analysis to provide additional insights and context on outlooks, underlying conditions driving current acquisition outlook and trends.
• Identifies data patterns & trends, and provides insights to enhance business decision making capability in business planning, process improvement, solution assessment, etc.
• Coordinate data gathering from external sources to inform analysis, and ensure that data is provided to appropriate teams to drive other analytic responsibilities.
• Cross-functional collaboration and driving programs to logic closure by identifying and rallying relevant teams, building consensus with functional and senior stakeholders and implementing recommendations.
• Work with marketing, finance, and risk teams to optimize, enhance, and expand programs to drive new accounts application efficiency relative to retail transactions.
• Partner with the portfolio Chief Analytics Officers to deliver analytic solutions to business initiatives.
• Work with local and offshore resources and provide analytic leadership and project management.
• Support Business and Global Data Analytics functions in Audits and Business Control activities.
• Appropriately assess risk in all business decisions, safeguarding Citigroup's reputation, clients, and assets through strict compliance with laws and policies, ethical conduct, and transparent reporting of control issues.
• Document data requirements, manage data collection, processing, and cleaning, and perform exploratory analysis using statistical models, algorithms, and visualization.

Business Analytics Lead Analyst – Product & Portfolio Analytics

• Build and deploy machine learning models to optimize revenue management and pricing strategies.
• Analyze large datasets to uncover insights and develop actionable recommendations for business decision-making.
• Collaborate with peers outside the data science team to align data science initiatives with business goals.
• Continuously evaluate and improve model performance through testing, tuning, and validation.
• Document processes, methodologies, and findings for both technical and non-technical audiences.

Senior Data Scientist

• Develop, enhance, and maintain predictive models (e.g., GLMs, machine learning, ensemble approaches) to support pricing, segmentation, and performance forecasting while balancing regulator considerations.
• Apply advanced statistical techniques to identify key drivers of sales, retention, and profitability, and forecast expected outcomes.
• Serve as a technical lead on larger, cross-functional initiatives.
• Lead scoped projects and drive them independently to completion, escalating questions or blockers appropriately.
• Manage a rotating portfolio of multiple active projects, including pricing model builds, research initiatives, and support for regulatory filings.
• Lead exploratory data analysis to evaluate relationships between data elements and insurance processes, including pricing, conversion, retention, and risk segmentation.
• Perform advanced root cause analysis to investigate data inconsistencies and system issues; collaborate with Data Engineering and Business Units to resolve findings.
• Translate complex technical findings into clear, actionable insights for leadership with varying technical backgrounds.
• Design, implement, and maintain scalable data pipelines to collect, process, and integrate structured and unstructured data from internal systems and external data sources.
• Create and maintain dashboards and reporting solutions (e.g., Power BI) to monitor key metrics such as rate level, risk segmentation, retention, and portfolio performance.
• Mentor and develop analytics talent within the team and across the organization.
• Stay current on industry trends, emerging technologies, and advancements in data science, analytics, and actuarial science.

• Serve as a technical thought leader in Data Science — defining principles, frameworks, and best practices for how Wonder uses data, experimentation, and machine learning to improve customer, marketplace, and business outcomes.
• Mentor and coach a growing team of Data Scientists and contribute to career development and technical excellence across the group.
• Lead the exploration of interconnected marketplace systems, recognizing feedback loops between customer behavior, fulfillment reliability, ETA accuracy, pricing, supply planning, product experience, and business performance.
• Develop causal inference and experimentation frameworks that help Wonder understand which product, operational, and marketplace changes truly drive business impact.
• Partner with engineering to drive architecture decisions for shared data layers, feature pipelines, modeling APIs, experimentation infrastructure, and production ML services.
• Define and implement robust experimentation strategies for changes that move business metrics in high-noise environments.
• Champion business-impact-driven data science, integrating causal inference, experimentation, risk-aware modeling, and scalable production ML systems that learn and adapt.

• shape the strategic direction of applied data science
• mentor senior and junior scientists
• collaborate closely with engineering, product, operations, and business leaders
• identify high-leverage opportunities across the business
• design statistically rigorous frameworks to understand causal impact
• guide business strategy through experimentation, measurement, and principled inference
• prototype, experiment, influence architecture, and ensure operationalization of models and insights that move business metrics

Senior Staff Data Scientist

• responsible for the engineering work necessary for successful creation, deployment and managing of AI capabilities of the Intelligent Delivery Platform
• ensuring data quality
• creation of new data pipelines
• optimization and management of existing data pipelines
• ingestion and curation of data sources for Gen AI purposes (including chunking/embedding strategies for RAG system)
• AI Agent delivery
• Prompt Engineering
• selection and configuration of AI-specific tools and platforms
• management and monitoring of AI models through MLOps tools and model ops practices
• operationalizing AI capabilities, working closely with larger team who will supplement where traditional application development support is needed

See all jobs on JobTailor

Lead Data Scientist

Manager, Data Scientist – Partnerships Acquisitions

Principal Associate, Data Scientist – Partnerships Acquisitions

Data Scientist I – Fraud Model Governance

Data Scientist

Lead Actuarial Data Scientist

Principal Associate, Data Science – Model Risk Office

Principal Associate, Data Scientist – Cash Flow Underwriting

CD&A Data Scientist

Ads AI Analytics Lead

Principal Data Scientist, Health Informatics

AI & GenAI Data Scientist – Manager

Senior Associate, AI & GenAI Data Scientist

Data Scientist

Business Analytics Lead Analyst – Product & Portfolio Analytics

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Staff Data Scientist

Lead Data Science Engineer