Lead the development and implementation of advanced data science models and algorithms, focusing on Agentic Commerce, LLM platforms, and behavioral modeling.
Compensation
The base pay for this role will depend on where you work and the relevant experience and expertise you bring. The expected range of pay for this role by location is: $144,991.00-243,500.00 per annum.
Responsibilities
Design and execute controlled experiments to measure the effectiveness of PayPal's Agentic Commerce offering across LLM platforms
Apply causal inference methodologies to attribute revenue and conversion changes to product features in AI-driven checkout flows
Develop behavioral models to understand merchant and consumer interactions within conversational commerce environments
Build measurement frameworks to track key performance metrics across growth, conversion, latency, availability, and friction for emerging AI commerce channels
Collaborate with Product and Engineering teams to optimize the Agentic Commerce experience based on data-driven insights
Requirements
Master’s degree or foreign equivalent, in Computer Science, Engineering, Data Science, Statistics, or a closely related field
A Data Scientist role focused transforming messy data into useful models and decision-making tools, collaborating with engineering and business development, and owning projects from scoping to deployment.
Compensation
The estimated base salary range for this position is $165,000 to $250,000, which is specific to New York and may change in the future. Millennium pays a total compensation package which includes a base salary, discretionary performance bonus, and a comprehensive benefits package.
Responsibilities
Own outcomes: break projects into milestones, estimate realistically, meet deadlines, and surface risks early with options.
Build and deploy models: develop ML models (including LLM-enabled solutions when appropriate), design features, evaluate rigorously, and contribute to production-grade pipelines.
Work the full data lifecycle: wrangle and clean data, implement data quality checks, write maintainable Python/SQL, add tests, and document decisions.
Collaborate cross-functionally: translate business questions into analysis, present trade-offs clearly, and iterate with stakeholders and tech partners.
Deploy to production: partner with data engineers to ship to production (APIs, batch jobs, monitoring) and create feedback loops for continuous improvement.
This job will lead the development and implementation of advanced data science models and algorithms, focusing on Agentic Commerce, LLM platforms, and causal inference.
Compensation
The expected range of pay for this role by location is: $144,991.00-243,500.00 per annum.
Responsibilities
Design and execute controlled experiments to measure the effectiveness of PayPal's Agentic Commerce offering across LLM platforms.
Apply causal inference methodologies to attribute revenue and conversion changes to product features in AI-driven checkout flows.
Develop behavioral models to understand merchant and consumer interactions within conversational commerce environments.
Build measurement frameworks to track key performance metrics across growth, conversion, latency, availability, and friction for emerging AI commerce channels.
Collaborate with Product and Engineering teams to optimize the Agentic Commerce experience based on data-driven insights.
Requirements
Master’s degree, or foreign equivalent, in Computer Science, Engineering, Data Science, Statistics, or a closely related field
Support client projects under the guidance of senior data scientists, applying data science techniques and building scalable models in a consulting environment.
Locations (2)
London, England, United Kingdom
Cheltenham, England, United Kingdom
Responsibilities
Support client projects under the guidance of senior data scientists.
Work in agile, iterative environments that encourage experimentation and learning.
Assist in understanding client business and technical needs to identify opportunities for data science.
Apply basic data science techniques to solve problems in a structured and robust way.
Help identify and prepare relevant data sources for analysis.
Requirements
Degree Educated or equivalent.
Nice to have: Exposure to machine learning concepts, such as classification, clustering, or model evaluation
Nice to have: Experience with data visualisation tools like Matplotlib, Seaborn, Power BI, or Tableau
Lead Data Scientist (GenAI) to design and deliver enterprise‐scale AI solutions using LLMs, RAG, and MCP. The role partners with business and technical leaders to build secure, scalable, high‐value GenAI capabilities across the organization.
Responsibilities
Lead GenAI architecture and design scalable solutions for enterprise use cases
Build modular LLM pipelines using LangChain, LlamaIndex, and similar frameworks
Define prompting, agent, and RAG strategies that can be reused across teams
Manage the full lifecycle of GenAI projects from development through deployment and MLOps
Ensure responsible AI including governance, compliance, and explainability
Requirements
Bachelor's degree in computer science, data science, ML, NLP, AI, linguistics, or related field
Apply scientific expertise to create, solve, and review challenging research problems within biology and related fields using Python.
Compensation
Compensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow. Minimum submission requirements apply.
Responsibilities
Create and solve technically rigorous scientific problems within your area of expertise.
Develop reproducible Python-based analyses, models, simulations, or data-processing workflows.
Review scientific solutions and code for correctness, clarity, and completeness.
The Pricing Analyst will turn pricing, customer, product, transaction, and market data into clear insight. The role is suited to someone with strong quantitative skills who enjoys statistical analysis, modelling, data validation, automation, and communicating findings to commercial stakeholders.
Responsibilities
Analyze pricing, customer, product, and market data to identify trends, opportunities, risks, and performance drivers.
Build and improve pricing models, forecasts, segmentation analysis, sensitivity analysis, and scenarios to support commercial decisions.
Develop repeatable reporting, data validation, and analytical workflows using Excel, Power Query, Python, SQL, and BI tools.
Support monthly and quarterly price forecasting, variance analysis, and commercial performance reviews.
Advanced degree in Mathematics, Statistics, Data Science, Engineering, Economics, Operations Research, or a related quantitative field; or a bachelor’s degree with approximately 2+ years of relevant analytics, pricing, commercial operations, finance analytics, or data science experience.
Analyze large and complex datasets to identify trends, patterns, and actionable insights; develop and implement machine learning models and algorithms; collaborate with cross-functional teams; design and maintain scalable data pipelines; communicate findings and recommendations; stay updated on latest advancements; ensure data quality, integrity, and security; optimize and fine-tune models for performance and scalability.
Compensation
Competitive salary and performance-based bonuses.
Responsibilities
Analyze large and complex datasets to identify trends, patterns, and actionable insights.
Develop and implement machine learning models and algorithms to solve business challenges.
Collaborate with cross-functional teams to understand business objectives and translate them into data-driven solutions.
Design and maintain scalable data pipelines and workflows for data collection, processing, and analysis.
Communicate findings and recommendations to stakeholders through clear visualizations and reports.
Requirements
Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field.
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