Data Scientist

• Solve business problems using data
• Analyze and interpret data to identify patterns or trends
• Pull data from SQL databases
• Visualize data using Tableau
• Present findings to stakeholders

Data Scientist II

• Establish and scale a centralized customer analytics and intelligence function, defining standards, ways of working, and governance to ensure consistency, quality, and scalability.
• Design the foundational capabilities, team structure, and operating model needed to support evolving business needs.
• Partner closely with marketing, distribution, and leadership to embed analytics into planning, prioritization, and decision-making, ensuring insights drive measurable business outcomes.
• Lead conversations and presentations with C-level executives.
• Oversee insights collection, analysis, and dissemination across regions and business units.
• Ensure data integrity, accuracy, and relevance in reporting.
• Translate complex data into clear, actionable insights for leadership and stakeholders.
• Support strategic decision-making with timely and relevant insights.
• Drive continuous improvement and innovation in analytics processes.
• Facilitate cross-functional collaboration to leverage data and insights.
• Develop and maintain a robust insight reporting framework.
• Identify emerging trends, opportunities, and risks through proactive analysis.
• Translate marketing, advertising and CX goals into actionable data strategies, advising on segmentation, personalization, and activation to improve targeting, ROI measurement, and customer experience.
• Enable sales with quality leads and distribution clarity within CRM.

Customer Data & Analytics Lead

• Collaborate with a cross-functional team of product managers, researchers, designers and engineers to develop and iterate on product features and strategy
• Translate product objectives to data science problems and vice versa
• Apply statistical inference to draw rigorous conclusions from data, especially through designing and analyzing A/B tests and working on strategic insights that drive product strategy
• Use a combination of exploratory analysis and data mining techniques to generate actionable insights about user experience and user problems
• Collaborate with AI team to develop, iterate, and test production models (e.g., targeting)

Senior Data Scientist, Matching

• Maintain data pipelines for automated data processing suitable for downstream reporting, tabling, and summarizing
• Execute and build upon reproducible reports via efficient code for complex tabling and data visualization.
• Contribute to internal or external R packages.
• Collaborate with Clinical Operations and Data Management teams to understand emerging trial and business needs, assess available data, and determine how to appropriately summarize and present results.

Data Scientist I

• Build and maintain Marathon's marketing attribution model, identifying which touchpoints across email, SMS, push, direct mail, and care team outreach are influencing activation, empanelment, and sustained engagement
• Develop standardized campaign performance dashboards for use by the marketing team for optimization
• Own funnel analysis across the full member journey from eligibility through activation, empanelment, and sustained engagement identifying where and why members drop off
• Design and analyze incrementality tests that isolate true lift from campaigns versus organic member behavior
• Partner with the lifecycle marketing manager and marketing automation team members to define test methodology, measurement plans, and success criteria across incrementality, A/B, and other experiment types, and own the analysis and reporting that turns results into clear optimization recommendations
• Serve as the analytical connective tissue across marketing automation, engagement enablement, product, data, and engineering teams, ensuring data flows correctly and is interpreted consistently

Marketing Analytics Lead

• Design and deploy machine learning, NLP, and generative AI solutions that help researchers discover, understand, and apply scientific knowledge.
• Build intelligent retrieval, search, recommendation, ranking, and question-answering systems that improve research outcomes.
• Develop AI systems that connect information across publications, datasets, citations, knowledge graphs, and scientific ontologies.
• Fine-tune, evaluate, and integrate large language models and retrieval-augmented generation (RAG) systems into production environments.
• Create robust evaluation frameworks that measure quality, reliability, relevance, trustworthiness, and user impact.
• Build scalable data pipelines and machine learning workflows that support experimentation, monitoring, and continuous improvement.
• Apply the appropriate combination of classical machine learning, deep learning, retrieval, and generative AI techniques to solve complex scientific problems.
• Collaborate with engineering, product, UX, analytics, and domain experts to transform ambiguous challenges into practical solutions.
• Contribute clean, maintainable, production-quality Python code and reusable AI components.
• Continuously improve the capabilities, performance, and real-world value of AI systems that support scientific discovery.

• Design, develop, and deploy advanced machine learning, NLP, retrieval, and generative AI solutions that support scientific discovery and knowledge exploration
• Build and optimize LLM-powered applications, including question answering, literature summarization, semantic search, research insight generation, and evidence-grounded AI experiences
• Develop retrieval-augmented generation (RAG) systems that connect AI models with trusted scientific and scholarly content
• Create intelligent capabilities for search, ranking, recommendation, entity extraction, classification, enrichment, and decision support
• Design evaluation frameworks that measure quality, relevance, reliability, grounding, trustworthiness, and user impact
• Integrate knowledge graphs, ontologies, taxonomies, citations, metadata, and scientific domain knowledge into AI workflows
• Partner with engineering teams to produce, monitor, optimize, and continuously improve AI systems at scale
• Lead technical discovery, influence solution architecture, and guide methodological decisions across initiatives
• Mentor fellow data scientists and contribute to a culture of technical excellence, experimentation, and responsible AI
• Collaborate closely with Product, Engineering, Research, Editorial, UX, and domain experts to solve complex scientific and business challenges

Senior Data Scientist

• Lead and develop high-performing teams of data scientists, machine learning engineers, researchers, and technical contributors.
• Define and execute data science strategies that advance scientific discovery, research intelligence, and knowledge-access products.
• Drive the development of AI-powered capabilities across search, retrieval, recommendation, NLP, knowledge systems, and generative AI.
• Translate complex customer, scientific, and business challenges into scalable data science solutions and measurable outcomes.
• Establish high standards for experimentation, evaluation, model quality, reliability, and responsible AI practices.
• Partner closely with Product, Engineering, Research, UX, Analytics, and domain experts to shape product strategy and delivery.
• Mentor and coach team members while fostering a culture of scientific rigor, collaboration, innovation, and continuous learning.
• Guide the adoption of emerging AI technologies, including LLMs, retrieval-augmented generation, semantic search, and knowledge-based systems.
• Influence senior stakeholders and contribute to long-term AI, technology, and product strategy across the organization.
• Ensure that AI systems are trustworthy, scalable, explainable, measurable, and aligned with meaningful customer and societal outcomes.

Data Science Manager

• Under limited supervision, the Data Scientist works closely with business stakeholders to understand how data can be used to achieve their goals.
• Designs data modeling processes, creates algorithms and predictive models and helps analyze the data and shares insights.
• Critical thinking and problem-solving skills are essential for interpreting data.
• Must have a passion for machine-learning and research.
• This position will be tasked with helping the company analyze trends to make better data-based decisions.
• Analyzes large amounts of information to discover trends and patterns.
• Designs machine learning models for predictive analytics.
• Presents information using data visualization techniques.
• Undertakes preprocessing of structured and unstructured data.
• Identifies valuable data sources and automates collection processes.
• Performs other duties as assigned.

Data Scientist, Finance

• 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

• Shape C&CE strategy by developing business cases, evaluating options, and assessing costs and benefits to guide prioritization decisions
• Leverage data to solve business problems by identifying data needs, pulling data from relevant sources, and performing analysis
• Identify business opportunities using trend analysis and mining operational data for insights
• Develop testing plans, monitor performance indicators, and be ready to quickly investigate and remediate emerging issues
• Grow into a thought partner and executive storyteller by distilling work into crisp presentations that educate, inform, and inspire action

Strategy & Analytics Lead Analyst

• 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

• You'll sit on our central Data Science & Analytics team but you’ll be embedded in one of Jerry's core product or business areas — growth, product, AI and automation, strategic partnerships — and own the analytical function for that business unit end-to-end.
• Define the right problems: Before any analysis, understand what business question it's answering and what decision it will inform. Push back if the question is flawed.
• Define metrics, build reports, run analyses, design experiments, and surface data insights that move your team's key metrics.
• Bring recommendations to your counterparts in product, growth, finance or operations, tell them what's happening and why, and what next steps should be.

Associate Data Scientist

• You'll sit on our central Data Science & Analytics team but you’ll be embedded in one of Jerry's core product or business areas — growth, product, AI and automation, strategic partnerships — and own the analytical function for that business unit end-to-end.
• Define the right problems: Before any analysis, understand what business question it's answering and what decision it will inform. Push back if the question is flawed.
• Data and insights: Define metrics, build reports, run analyses, design experiments, and surface data insights that move your team's key metrics.
• Drive outcomes: Bring recommendations to your counterparts in product, growth, finance or operations, tell them what's happening and why, and what next steps should be.

• Define the right problems: Before any analysis, understand what business question it's answering and what decision it will inform. Push back if the question is flawed.
• Data and insights: Define metrics, build reports, run analyses, design experiments, and surface data insights that move your team's key metrics.
• Drive outcomes: Bring recommendations to your counterparts in product, growth, finance or operations, tell them what's happening and why, and what next steps should be.

Staff Data Scientist

• Lead the architecture and technical strategy for large-scale retrieval, ranking, vector search, RAG, and anomaly detection systems operating at enterprise scale
• Drive innovation across LLMs, AI-powered discovery, personalization, and autonomous AI workflows
• Define the future direction of AI-driven reasoning, ranking and investigation capabilities across the platform
• Own end-to-end AI/ML architecture, including data pipelines, feature engineering, model development, deployment, monitoring, evaluation, and continuous improvement
• Establish scalable architecture patterns, engineering standards, experimentation frameworks, and operational best practices across multiple teams
• Drive foundational investments and technical direction across AI platforms, retrieval infrastructure, model-serving systems, and ML tooling
• Balance model quality, customer experience, latency, scalability, reliability, security, and infrastructure cost when making architectural decisions
• Define evaluation methodologies, experimentation strategies, and success metrics to assess product, model, and business impact
• Partner closely with Product, Engineering, Security, Infrastructure, and Operations teams to identify high-value opportunities and deliver scalable AI solutions
• Communicate technical trade-offs, recommendations, and results clearly to both technical and executive audiences
• Lead highly ambiguous, multi-quarter initiatives spanning Product, Engineering, Data Science, and other stakeholders
• Influence organization-wide technical strategy, investment priorities, and long-term AI roadmap decisions
• Represent the organization in executive reviews and drive alignment on major technical and product initiatives
• Mentor junior scientists while fostering a culture of technical excellence and innovation

Senior Data Scientist – Hybrid

• 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-functionally to complete the build phase, supporting system configuration design, testing, migration and hyper-care activities
• Complete conversion functional specification documents, value mapping, cross reference documents and data model documents across multiple deployments
• Drive conversion progress, including task completion and milestone achievement to adhere to overall timelines
• Lead meetings with cross functional topic leads
• Understand 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

SAP S/4 HANA Data Lead Analyst

• 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

• Perform data profiling, reconciliation, and validation to ensure accuracy, completeness, and consistency across procurement datasets
• Analyze data discrepancies, identify root causes, and implement corrective measures with detailed documentation
• Execute post-load validation checks and ensure adherence to SAP data standards, governance policies, and quality frameworks
• Design and optimize scalable data workflows for efficient extraction, transformation, and processing
• Build and deploy ML-based supplier scoring models to automate vendor evaluation and shortlisting
• Develop NLP and LLM-powered pipelines to analyze proposals, SOW documents, and other sourcing artifacts
• Leverage LLMs to automate RFx document generation and review, ensuring consistency, completeness, and alignment with business requirements
• Automate market research workflows using web scraping and generative AI to generate supplier landscape reports and insights
• Design procurement analytics dashboards and predictive models to track sourcing pipeline health and forecast cycle timelines, utilizing SQL, Power BI, or Tableau
• Implement anomaly detection models on procurement spend data to identify contract leakage, while applying NLP techniques (NER, embeddings, RAG) and leveraging LLM APIs (OpenAI, AWS Bedrock, Azure OpenAI), with proficiency in Python and ML frameworks (scikit-learn, spaCy, LangChain) to enable end-to-end procurement automation

See all jobs on JobTailor

Data Scientist II

Customer Data & Analytics Lead

Senior Data Scientist, Matching

Data Scientist I

Marketing Analytics Lead

Data Scientist

Senior Data Scientist

Data Science Manager

Data Scientist, Finance

Lead Data Scientist

Strategy & Analytics Lead Analyst

Data Scientist – FCRM

Associate Data Scientist

Data Scientist

Senior Data Scientist

Staff Data Scientist

Senior Data Scientist – Hybrid

SAP S/4 HANA Data Lead Analyst

Senior Data Science Lead

Data Science – Gen AI Engineer