We're a leading SaaS provider revolutionising how fintech companies navigate complex regulatory landscapes. Our platform powers compliance and risk management for hundreds of innovative financial services companies, from emerging startups to established enterprises. We believe in empowering our teams to solve meaningful problems and create exceptional value for our customers.

Role Summary

As a Senior Data Scientist at ComplyAdvantage, you will lead the development and refinement of sophisticated data-driven solutions to detect and prevent financial crime globally. Your core focus is R&D and Strategic Initiatives, where you will spend 80% of your time conducting forward-looking research in ML, NLP, and Agentic AI to create measurable business value over 3-6 month horizons. In addition to driving deep-dive R&D, you will provide expert advisory support and technical leadership to Product and Engineering teams. By leading AI Enablement workstreams, you will empower these departments with the quantitative insights and frameworks necessary to scale high-impact, AI-driven features while mentoring more junior members of the tribe.

Key Responsibilities

• Lead R&D in GenAI and Agentic AI: Design and prototype high-impact, novel solutions for financial crime detection, focusing on multi-agent systems and RAG architectures built on foundational LLM frameworks.
• Drive Production Excellence: Partner with Engineering and Product leadership to architect and oversee the integration of AI components into core product features. You will define the standards for reliability, scalability, and ethical AI performance, ensuring research prototypes transition into robust, production-grade solutions.
• Establish Evaluation Frameworks: Lead the development of robust evaluation methodologies for LLM and agentic AI systems, defining industry-standard metrics for task completion, reasoning accuracy, and system reliability.
• Evaluate Model Context Protocol (MCP): Research applications to connect our AI systems with external data sources and tools, providing strategic recommendations for engineering implementation.
• Strategic Data Insights: Conduct advanced exploratory analysis across multiple data sources to uncover insights that directly influence product development decisions and agent behavior.
• Research & Innovation: Continuously research and evaluate emerging tools and best practices in data science and Agentic AI to keep ComplyAdvantage at the forefront of the field.
• Technical Leadership & Mentoring: Engage across multi-disciplinary teams to share expertise, perform code reviews, and provide career development support to junior data scientists.

Required Qualifications

• MSc or PhD in a numerate subject (e.g., Computer Science, Mathematics, Statistics, Physics) or equivalent practical experience in a data-intensive role.
• 4-7 years of professional experience in data science or machine learning, with a demonstrable impact on production systems.
• Expert-level proficiency in Python, including the core data science ecosystem such as Pandas, NumPy, and Scikit-learn.
• Deep experience with modern ML frameworks (e.g., TensorFlow, PyTorch) for advanced research and prototyping.
• Strong expertise in Agentic AI and LLMs: Advanced knowledge of agentic AI frameworks (e.g., LangChain, Pydantic AI) and experience optimising retrieval systems like RAG.
• Experience with Distributed Computing: Practical knowledge of platforms like Spark or Dask for large-scale data analysis.
• Technical Leadership: Proven track record of leading technical research projects with successful production outcomes and communicating complex concepts to diverse audiences.

Preferred Qualifications

• Experience delivering machine learning solutions and integrating into data pipelines.
• Experience with Natural Language Processing, Entity Resolution, Graph Theory, or Graph Machine Learning.
• Experience working with or evaluating multi-agent systems or complex GenAI workflows.

What Success Looks Like

• In 90 days: You have successfully onboarded, mastered our data landscape, and begun contributing to active research projects while building relationships with key stakeholders.
• In 6 months: You have delivered your first significant research project with measurable impact and established yourself as a technical leader and effective mentor.
• In 12 months: You have led multiple successful R&D initiatives that have shaped our technical roadmap and been recognized as a subject matter expert who empowers other teams.

About Data Science at ComplyAdvantage:
The Data Science tribe discovers and proves out impactful solutions that leverage our Petabyte-scale data infrastructure to neutralise global financial crime. We are responsible for research and development work that drives the "art of the possible" within our product suite. At ComplyAdvantage, we mine the internet for critical data and use it to power a dynamic knowledge graph, which uncovers hidden networks of financial bad actors. From exploring real-time stream processing with Kafka to pioneering the use of the latest LLMs, our modern technology stack allows us to innovate and build prototypes for cutting-edge agentic AI and large-scale algorithmic solutions that empower our clients.

What’s in it for you? 

• Equity as we want you to have a part of what we are building 
• Private medical insurance designed to keep you ensuring peace of mind while you excel in your career
• Unlimited Time Off Policy- A work-life balance and focus on our well-being are critical to keeping us performing at our best 
• We embrace a hybrid approach that requires employees to be in the office for two days a week. We strongly believe that this approach fosters collaboration and enables the building of meaningful relationships
• You will also get a new starter budget to kit out your home office 
• Opportunity to work on innovative projects with smart-minded people keen to share their knowledge and continuously improve 
• Annual learning budget (prorated based on start date) to drive your performance and career development 

The Opportunity

As a Staff Data Scientist at PandaDoc, you will serve as a senior analytical leader, embedding yourself deeply in our product and business data to uncover non-obvious insights and drive actionable recommendations. A primary focus of this strategic role is to champion and drive the organizational shift toward a data-driven culture. You will own the advancement of our experimentation capabilities, train other analysts and data scientists on causal methodologies, and leverage your expertise to provide leadership with a clear, reliable understanding of true impact and causality.

You will report to the Director of GTM Data and act as a strategic thought partner to Go-to-Market teams, Marketing, Product, Finance, Design, Engineering, and executive leadership, ensuring alignment between data insights and critical business decisions.

What You'll Do

Experimentation & Causal Strategy

• Lead the Experimentation Roadmap: Define, champion, and execute a strategic roadmap for measuring impact across PandaDoc, focusing on high-leverage business questions related to customer workflows, churn risk, and long-term value (LTV).
• Advanced Experiment Design: Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods, including the application of Bayesian experimentation, to assess the effectiveness of proposed product changes and business levers.
• Causal Inference Beyond A/B: Apply advanced causal inference techniques (e.g., difference-in-differences, synthetic control, propensity score matching, and instrumental variables) to scenarios where randomized controlled trials (RCTs) are infeasible.
• Deep Dive Analysis: Conduct complex, proactive, and exploratory analysis to discover latent user behavior, emerging trends, and root causes of changes in key metrics, translating these findings into actionable product and business insights.
• Develop Measurement Frameworks: Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps low-level product health metrics to high-level business outcomes, ensuring consistent and scalable measurement across the organization.

Technical Leadership & Influence

• Scaling Data Science: Partner with Data Engineering to design and build scalable, self-serve experimentation tooling and reusable analytical assets and frameworks (e.g., causal machine learning models) that empower other analysts and data consumers.
• Strategic Influence: Act as a strategic thinker by translating complex statistical findings into clear, compelling, and actionable business narratives for cross-functional partners and senior leadership (VP/C-suite), driving strategic decisions and investment priorities.
• Mentorship and Training: Serve as a technical subject matter expert, training and mentoring junior and mid-level data scientists on best practices in statistical rigor, experimental design, and causal modeling.

About You

Qualifications

• Experience: 6+ years of professional experience in an applied data science, economics, or product analytics role, with a proven track record of leveraging experimentation and causal inference methods to drive significant business impact.
• Education: B.A. or B.S. in Mathematics, Statistics, Economics, Computer Science, or a related quantitative discipline. A Master’s degree in a quantitative field (e.g., Statistics, Data Science, Econometrics, Operations Research) is preferred, but not required.

Required Technical Expertise

• Causal Inference: Demonstrated expertise in applying a wide range of Causal Inference methods, e.g. Quasi-Experimentation, Matching Methods (PSM), Difference-in-Differences, and/or Instrumental Variables.
• Experimentation Methodologies: Expertise in advanced statistical methodologies for A/B testing, including sample size calculations, sequential testing, dealing with interference/network effects, variance reduction techniques (e.g., CUPED), etc.
• Deep Analytical Methods: Mastery of advanced statistical modeling, time-series analysis, and quantitative methods necessary to perform thorough exploratory data analysis, produce timely insights, and provide actionable recommendations.
• Programming: Advanced proficiency in Python or R for statistical modeling, with experience using relevant data science packages (e.g., SciKit-Learn, numpy, pandas).
• Data Tools: Expert-level proficiency in SQL and experience working with established data warehouses (e.g., Snowflake, Postgres).
• Data Pipelining: Experience with data transformation and workflow management tools such as dbt, Airflow, or Databricks is a strong plus.

Key Attributes

• Strategic Communication & Influence: Possesses exceptional communication, presentation, and data storytelling skills with a consistent record of influencing cross-functional partners and leadership at all levels, particularly in navigating and driving consensus in unstructured or ambiguous environments.
• Change Management: Proven ability to drive organizational change management in environments where experimentation and data-driven decision-making are not yet widely adopted.
• Thrive in ambiguity: Ability to navigate significant ambiguity, translate complex business questions into clear analytical frameworks, and manage multiple competing priorities in a fast-paced environment.
• Relevant Experience: Experience in a SaaS domain and a strong focus on Product Data Science are strongly preferred.

Company Culture: 

• We're known for our work-life balance, kind co-workers, & creative virtual team-bonding events. And although our Pandas are located across the globe, we stay connected with the help of technology and ensure that everyone on our team feels, well, like a team.
• Pandas work best when they're happy. We retain our talent by upholding our values of integrity & transparency, and selling a product that changes the lives of our customers. 
• Check out our LinkedIn to learn more. 

Benefits:

• Our benefits include tremendous career growth opportunities, a competitive salary, health and commuter benefits, company paid life & disability, 20+ PTO days, 401K and FSA plans, and of course, a fun team of Pandas to work with!

PandaDoc is an Equal Opportunity Employer. We are committed to equal treatment of all employees without regard to race, national origin, religion, gender, age, sexual orientation, veteran status, physical or mental disability or other basis protected by law.

Role Summary

As a Data Scientist at ComplyAdvantage, you will develop and refine data-driven solutions to detect and prevent financial crime globally. Your core focus is R&D and Strategic Initiatives, conducting forward-looking research in ML, NLP, and Agentic AI to create measurable business value. In addition to deep-dive R&D, you will provide advisory support to Product and Engineering teams. By investing in AI Enablement workstreams, you will empower these departments with the quantitative insights and frameworks necessary to scale high-impact, AI-driven features.

Key Responsibilities

• Advance R&D in GenAI and Agentic AI: Develop high-impact prototypes for financial crime detection, leveraging multi-agent systems and RAG architectures built on foundational LLM frameworks.
• Contribute to Core Solutions: Collaborate with Engineering and Product to integrate and optimise AI components and frameworks into existing and new product features, ensuring reliability, scalability, and ethical performance in production.
• Evaluate LLM-based Systems: Contribute to the development and implementation of robust evaluation methodologies for LLM and agentic AI based systems, tracking key metrics related to task completion, reasoning accuracy, tool effectiveness, and overall reliability.
• Evaluate Model Context Protocol (MCP) applications: Connect our AI systems with external data sources and tools and provide recommendations for engineering implementation
• Data Understanding: Develop a solid understanding of our existing data sources, identifying challenges and suggesting potential improvements.
• Data Analysis: Use exploratory data analysis and data mining techniques to identify actionable insights for product improvement, including patterns that can inform and refine agent behavior and prompt engineering strategies.
• Research & Innovation: Continuously research and evaluate emerging tools, technologies, and best practices in data science, especially within the rapidly evolving field of Agentic AI, to keep ComplyAdvantage at the forefront.
• Cross-Functional Collaboration: Engage effectively with multi-disciplinary teams to actively share best practices, expertise and inspire innovation to foster an AI-first culture across the organization.
• Visualization & Roadmapping: Provide clear data insights and visualizations that will act as a basis for new ideas to be integrated into our product roadmap.

Required Qualifications

• MSc or PhD in a numerate subject (e.g., Computer Science, Mathematics, Statistics, Physics) or equivalent practical experience in a data-intensive role.
• 2-4 years of professional experience in data science or a related field, with a demonstrated ability to contribute to impactful data-driven solutions.
• Proficiency in Python, including experience with core data science libraries (e.g., Pandas, NumPy, Scikit-learn).
• Hands-on experience with modern machine learning frameworks such as TensorFlow or PyTorch.
• Familiarity with the foundational concepts of agentic AI and large language models (LLMs). This can be professional or through personal research.
• Basic understanding of popular agentic AI frameworks (e.g., LangChain, Pydantic AI).
• Knowledge of general evaluation techniques for AI models and an eagerness to learn and apply methods specific to agentic systems.
• Ability to communicate complex technical concepts effectively to a variety of stakeholders.
• Strong passion for data and solving challenging real-world problems using algorithms.

Preferred Qualifications

• Prior experience contributing to the deployment and integration of machine learning models into production data pipelines.
• Exposure to Big Data platforms such as Spark.
• Experience with Natural Language Processing, Entity Resolution, Graph Theory, or Graph Machine Learning.
• Experience working with or evaluating multi-agent systems or complex AI workflows.

What Success Looks Like

• In 90 days: You have successfully onboarded, gaining a clear understanding of our data landscape and current AI frameworks. You are actively contributing to R&D experiments and have established collaborative working relationships with peers in Engineering and Product.
• In 6 months: You have delivered your first significant R&D prototype or technical evaluation. You have begun contributing to our AI Enablement workstreams, perhaps by authoring a technical blog post or internal guide that translates your research findings into actionable knowledge for the wider team.
• In 12 months: You have completed multiple R&D initiatives that have shaped our technical roadmap. You are recognized for empowering other teams through your advisory support and quantitative expertise, consistently providing the "AI-first" insights that help Engineering and Product teams scale high-impact features.

About Data Science at ComplyAdvantage:
The Data Science tribe discovers and proves out impactful solutions that leverage our Petabyte-scale data infrastructure to neutralise global financial crime. We are responsible for research and development work that drives the "art of the possible" within our product suite. At ComplyAdvantage, we mine the internet for critical data and use it to power a dynamic knowledge graph, which uncovers hidden networks of financial bad actors. From exploring real-time stream processing with Kafka to pioneering the use of the latest LLMs, our modern technology stack allows us to innovate and build prototypes for cutting-edge agentic AI and large-scale algorithmic solutions that empower our clients.

What’s in it for you? 

• Equity as we want you to have a part of what we are building 
• Private medical insurance designed to keep you ensuring peace of mind while you excel in your career
• Unlimited Time Off Policy- A work-life balance and focus on our well-being are critical to keeping us performing at our best 
• We embrace a hybrid approach that requires employees to be in the office for two days a week. We strongly believe that this approach fosters collaboration and enables the building of meaningful relationships
• You will also get a new starter budget to kit out your home office 
• Opportunity to work on innovative projects with smart-minded people keen to share their knowledge and continuously improve 
• Annual learning budget (prorated based on start date) to drive your performance and career development 

The role

Fraud prevention is an ever-evolving puzzle, making it one of the most rewarding and impactful areas in fintech. At GoCardless, our Fraud Prevention team develops the intelligent systems that protect our platform, our merchants, and their customers.

As a Senior Data Scientist, you will guide the design and delivery of models that operate in real time across our global payment network. You’ll work at the intersection of machine learning, graph-based detection, and behavioural modelling to stay ahead of changing patterns turning research into production systems that make a tangible difference in the fight against financial crime.

You’ll collaborate closely with Engineers, Fraud Analysts, and Product Managers to help shape the technical roadmap as we expand into new markets. Our stack is centred around Google Cloud Platform and Vertex AI, using Python, SQL, and BigQuery to build and support high-performance models at scale.

What you'll do

• Contribute to the full model lifecycle, from initial discovery and feature engineering through to production, experimentation, and continuous monitoring.
• Design and refine real-time ML systems that reduce false positives and ensure a smooth, secure experience for legitimate customers.
• Foster a responsive feedback loop, adapting models as patterns evolve to ensure our defences remain robust and effective.
• Help shape the technical direction of fraud prevention ML at GoCardless, exploring new approaches as we grow globally.
• Support the team’s growth through technical mentorship, knowledge sharing, and a commitment to high-quality code.

What excites you

• Solving complex, dynamic problems: Developing systems that can adapt and respond to changing environments.
• End-to-end ownership: Seeing a project through from a conceptual business need to a live, impactful solution.
• Strategic influence: Contributing to the ML roadmap and helping determine what we build next, rather than just how we build it.
• Holistic Data Science: Engaging in deep-dive analysis, prototyping, and live experimentation.
• Modern Infrastructure: Building production-grade models on GCP and Vertex AI, with the flexibility to explore advanced architectures.

What excites us

• You hold a degree (or PhD) in a STEM discipline, or bring equivalent depth through commercial experience.
• You have hands-on experience with modelling approaches such as deep learning, graph-based methods, or sequence models. While fintech experience is great, we value transferable skills from fields like cybersecurity or risk modelling.
• You have a track record of deploying models in production that create measurable value.
• You can translate complex ML concepts into clear, practical solutions for stakeholders across the business.
• You enjoy writing clean code and helping the team thrive through thoughtful reviews and shared learning.

The role

Data sits at the core of our mission. We leverage bank account data to deliver high-value, intelligent payment solutions for our customers, from enhancing payment success rates to driving payer fraud prevention.

As a Senior Data Scientist within our Payment Intelligence team, you’ll partner with Software Engineers, Product Managers, and Designers to turn big ideas into reality. You’ll own the full lifecycle of our algorithms, shaping everything from the initial concept to production-ready code that powers our global payment network.

At GoCardless, our stack is centered around Google Cloud Platform and Vertex AI, providing a high-performance environment for innovation. Our Data Scientists operate at the intersection of Python, SQL, and BigQuery to build and deploy high-performance models at scale.

What you’ll do

• Lead the end-to-end delivery of models at scale, from initial discovery and feature engineering to production, A/B testing and continuous monitoring.
• Collaborate with product and engineering peers to turn complex data into real-time, mission-critical fraud prevention solutions.
• Raise the team’s collective bar through hands-on technical leadership and knowledge sharing.
• Proactively research and integrate latest developments in ML and payer fraud prevention to drive innovation at GoCardless.

What excites you

• Being a self-starter who thrives on taking a vague business problem and owning the journey from the first prototype to a live, measurable solution.
• An opportunity to introduce and scale advanced architectures to solve high-stakes fraud and payment challenges.
• Moving beyond execution to help define the technical roadmap for Payer Fraud Prevention, ensuring we leverage the best tools for the job.
• Building production-grade ML models on a streamlined GCP and Vertex AI stack to drive fintech innovation.

What excites us

• You hold a degree (or PhD) in a STEM discipline or an equivalent commercial experience.
• You bring hands-on experience with sophisticated architectures, such as deep learning, graph-based, or sequence-based models (experience in Fintech, Fraud Prevention, or Payments is a big plus).
• You can translate complex ML concepts into practical product solutions and communicate these ideas clearly to non-technical peers.
• You are comfortable owning the full lifecycle, from deep-dive analysis and feature engineering to prototyping, validation, and live A/B testing.
• You lead by example, writing clean, high-quality code and raising the team’s technical bar through knowledge sharing and code best practices.

The Opportunity

As a Staff Data Scientist at PandaDoc, you will serve as a senior analytical leader, embedding yourself deeply in our product and business data to uncover non-obvious insights and drive actionable recommendations. A primary focus of this strategic role is to champion and drive the organizational shift toward a data-driven culture. You will own the advancement of our experimentation capabilities, train other analysts and data scientists on causal methodologies, and leverage your expertise to provide leadership with a clear, reliable understanding of true impact and causality.

You will report to the Director of Product Data and act as a strategic thought partner to Product, Finance, Design, Engineering, Product Marketing, and executive leadership, ensuring alignment between data insights and critical business decisions.

What You'll Do

Experimentation & Causal Strategy

• Lead the Experimentation Roadmap: Define, champion, and execute a strategic roadmap for measuring impact across PandaDoc, focusing on high-leverage business questions related to customer workflows, churn risk, and long-term value (LTV).
• Advanced Experiment Design: Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods, including the application of Bayesian experimentation, to assess the effectiveness of proposed product changes and business levers.
• Causal Inference Beyond A/B: Apply advanced causal inference techniques (e.g., difference-in-differences, synthetic control, propensity score matching, and instrumental variables) to scenarios where randomized controlled trials (RCTs) are infeasible.
• Deep Dive Analysis: Conduct complex, proactive, and exploratory analysis to discover latent user behavior, emerging trends, and root causes of changes in key metrics, translating these findings into actionable product and business insights.
• Develop Measurement Frameworks: Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps low-level product health metrics to high-level business outcomes, ensuring consistent and scalable measurement across the organization.

Technical Leadership & Influence

• Scaling Data Science: Partner with Data Engineering to design and build scalable, self-serve experimentation tooling and reusable analytical assets and frameworks (e.g., causal machine learning models) that empower other analysts and data consumers.
• Strategic Influence: Act as a strategic thinker by translating complex statistical findings into clear, compelling, and actionable business narratives for cross-functional partners and senior leadership (VP/C-suite), driving strategic decisions and investment priorities.
• Mentorship and Training: Serve as a technical subject matter expert, training and mentoring junior and mid-level data scientists on best practices in statistical rigor, experimental design, and causal modeling.

About You

Qualifications

• Experience: 6+ years of professional experience in an applied data science, economics, or product analytics role, with a proven track record of leveraging experimentation and causal inference methods to drive significant business impact.
• Education: B.A. or B.S. in Mathematics, Statistics, Economics, Computer Science, or a related quantitative discipline. A Master’s degree in a quantitative field (e.g., Statistics, Data Science, Econometrics, Operations Research) is preferred, but not required.

Required Technical Expertise

• Causal Inference: Demonstrated expertise in applying a wide range of Causal Inference methods, e.g. Quasi-Experimentation, Matching Methods (PSM), Difference-in-Differences, and/or Instrumental Variables.
• Experimentation Methodologies: Expertise in advanced statistical methodologies for A/B testing, including sample size calculations, sequential testing, dealing with interference/network effects, variance reduction techniques (e.g., CUPED), etc.
• Deep Analytical Methods: Mastery of advanced statistical modeling, time-series analysis, and quantitative methods necessary to perform thorough exploratory data analysis, produce timely insights, and provide actionable recommendations.
• Programming: Advanced proficiency in Python or R for statistical modeling, with experience using relevant data science packages (e.g., SciKit-Learn, numpy, pandas).
• Data Tools: Expert-level proficiency in SQL and experience working with established data warehouses (e.g., Snowflake, Postgres).
• Data Pipelining: Experience with data transformation and workflow management tools such as dbt, Airflow, or Databricks is a strong plus.

Key Attributes

• Strategic Communication & Influence: Possesses exceptional communication, presentation, and data storytelling skills with a consistent record of influencing cross-functional partners and leadership at all levels, particularly in navigating and driving consensus in unstructured or ambiguous environments.
• Change Management: Proven ability to drive organizational change management in environments where experimentation and data-driven decision-making are not yet widely adopted.
• Thrive in ambiguity: Ability to navigate significant ambiguity, translate complex business questions into clear analytical frameworks, and manage multiple competing priorities in a fast-paced environment.
• Relevant Experience: Experience in a SaaS domain and a strong focus on Product Data Science are strongly preferred.

Company Culture: 

• We're known for our work-life balance, kind co-workers, & creative virtual team-bonding events. And although our Pandas are located across the globe, we stay connected with the help of technology and ensure that everyone on our team feels, well, like a team.
• Pandas work best when they're happy. We retain our talent by upholding our values of integrity & transparency, and selling a product that changes the lives of our customers. 
• Check out our LinkedIn to learn more. 

Benefits:

• Our benefits include tremendous career growth opportunities, a competitive salary, health and commuter benefits, company paid life & disability, 20+ PTO days, 401K and FSA plans, and of course, a fun team of Pandas to work with!

PandaDoc is an Equal Opportunity Employer. We are committed to equal treatment of all employees without regard to race, national origin, religion, gender, age, sexual orientation, veteran status, physical or mental disability or other basis protected by law.