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Principal Data Scientist
Full Remote (From the US)
Salary up to $210K base + bonus

A large, global technology-led organisation operating in a mission-driven, data-intensive domain. The business designs and scales complex digital platforms used by millions of end users worldwide, combining advanced data science, AI, and product engineering to drive measurable real-world impact.

Mission

• Act as technical lead for high-impact Data Science and AI initiatives across the organisation
• Define architectures, methodologies, and best practices for complex DS/AI and GenAI problem spaces
• Lead the design and delivery of production-grade AI systems, with particular focus on LLMs and generative AI
• Drive strategic initiatives by aligning DS/AI priorities with business and product objectives
• Mentor senior and junior data scientists, setting technical direction and raising engineering standards
• Own multiple projects simultaneously: scoping, prioritisation, delivery timelines, and stakeholder alignment

Profile

• PhD or equivalent in Data Science, Applied Mathematics, Statistics, or related field
• Senior-level experience: typically 6-10+ years in DS/AI roles, including leadership at Principal / Staff level
• Demonstrated technical leadership: defining strategy, owning complex initiatives, and mentoring teams
• Deep hands-on experience with production-scale DS/AI systems, with a strong recent focus on Generative AI / LLMs
• Expertise in LLM architectures, orchestration frameworks, and AI governance (guarding, scaling, monitoring, reliability)
• Strong understanding of MLOps / LLMOps in enterprise environments

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Principal Data Scientist

Remote | Salary up to $220,000 + 15% bonus

This is an opportunity to step into a senior, high-impact Principal Data Scientist role where you will shape the direction of advanced AI and data science initiatives at scale. You will work on commercially meaningful problems, leveraging cutting-edge techniques including generative AI, while influencing both strategy and delivery across a globally recognised platform.

The Company
They are a well-established, innovation-led organisation focused on delivering data-driven products at scale. With a strong investment in AI and advanced analytics, they partner closely across product, engineering, and design to create impactful, user-focused solutions. Their teams are highly collaborative and operate in a modern, fully remote environment that encourages autonomy and technical excellence.

The Role

• Lead the design and implementation of advanced data science and AI solutions aligned to business objectives
• Provide technical leadership across complex projects, setting direction and best practice for modelling and deployment
• Collaborate with cross-functional stakeholders to identify opportunities and define requirements
• Apply generative AI and other advanced methodologies to solve real-world problems
• Translate technical findings into clear, actionable insights for senior stakeholders
• Oversee project timelines and ensure successful delivery across multiple workstreams

Your Skills and Experience

• Strong commercial experience in data science and machine learning, with recognised expertise in a specialist area
• Deep understanding of production-level AI systems and recent exposure to generative AI techniques
• Proven ability to lead complex projects end to end, from concept through to deployment
• Experience working cross-functionally with product, engineering, or business teams
• Clear and collaborative communication style, with the ability to influence senior stakeholders
• Background in a quantitative or technical field such as Data Science, AI, Mathematics, or similar

What They Offer

• Salary up to $220,000 with a 15% performance bonus
• Fully remote working with flexibility and autonomy
• Opportunity to work on high-impact AI initiatives at scale
• Collaborative, forward-thinking technical environment
• Clear scope for progression into broader technical or strategic leadership roles

How to Apply
If you’re interested in exploring this Principal Data Scientist opportunity, apply now for a confidential discussion.

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Staff Data Scientist

Location: New York City (Hybrid)

Compensation: Up to $250,000 base + bonus + equity

Company Overview

A high-growth consumer fintech and e-commerce platform is building the credit infrastructure powering digital commerce in a large, underserved market. The business has reached profitability, processes hundreds of millions in annual transaction volume, and continues to scale rapidly with strong backing from top-tier investors.

The team is lean, highly technical, and composed of leaders from globally recognized technology and marketplace companies. This is an opportunity to join at a pivotal stage and directly influence core revenue-driving systems.

The Role

As a Staff Data Scientist, you will play a critical role in developing and deploying machine learning models that directly impact the company’s P&L. You’ll work across credit risk, pricing, and marketplace optimization problems, owning the full lifecycle from problem definition through to production.

This is a highly cross-functional role partnering with engineering, product, and leadership to drive data-informed decisions and scalable modeling solutions.

Key Responsibilities

• Build and deploy machine learning models for underwriting, credit risk, and portfolio optimization
• Develop pricing, ranking, and personalization algorithms to improve marketplace performance
• Apply causal inference and experimentation techniques to optimize decision-making
• Own projects end-to-end: from exploratory analysis and modeling through to production deployment
• Translate complex modeling outputs into clear business insights and recommendations
• Collaborate closely with engineering and product teams to operationalize models

Requirements

• 5+ years of experience in data science or machine learning in a production environment
• Strong foundation in statistical modeling and machine learning (e.g., classification, ensemble methods)
• Experience deploying models into production and iterating based on real-world performance
• Proficiency in Python and SQL
• Experience with experimentation, causal inference, or uplift modeling
• Strong problem-solving skills with the ability to operate in ambiguous, fast-paced environments

Preferred Background

• Advanced degree (PhD or Master’s) in a quantitative field such as Statistics, Mathematics, Economics, or Operations Research
• Experience in fintech, lending, or credit risk modeling
• Exposure to marketplace, pricing, or recommendation systems
• Familiarity with optimization techniques and constrained modeling problems

What Makes This Opportunity Unique

• Direct ownership of models that impact revenue and risk
• High visibility role working closely with senior leadership
• Fast-paced, startup environment with significant autonomy
• Opportunity to shape core data science strategy and systems

• If you’re excited by building high-impact machine learning systems in a fast-moving environment and want to see your work directly drive business outcomes, this is a unique opportunity to do so at scale.

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Responsibilities:

• Lead the design and implementation of technical solutions in alignment with organizational goals.
• Define and maintain an overview of projects and timelines across contributing workstreams.
• Collaborate with other departments and stakeholders to identify data science needs, define project requirements, and ensure data science and psychometric initiatives are aligned with business objectives.
• Present findings and recommendations to key stakeholders.

Education Requirement:

Doctoral degree (or equivalent) in AI, Data Science, or related field

Skills and Experience:

Required:

• Recognized expertise in at least one area of DS/AI through academic and industry work
• Deep knowledge and practical experience with production-scale DS/AI systems with a recent focus on generative AI (GenAI)
• Understanding of complex GenAI architectures and experience with scaling, monitoring, and maintaining these systems
• RAG and LLM based implementation
• Strong Python experience
• Must have end to end project experience and be able to demonstrate what impact you/the project had on the business
• Demonstrated ability to independently define and successfully complete projects
• Demonstrated technical leadership capability
• Clear, collaborative communication style
• Advanced knowledge in a specialization with an understanding of cross-disciplinary concepts and emerging trends, enabling collaboration across teams and domains
• Experience supporting DS/AI systems across stages of the Machine Learning Operations/LLM Operations lifecycle at enterprise scale

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Staff Data Scientist (Product)

Location: Remote

Salary: $190-220k

We’re partnering with a fast-growing SaaS company to find a Staff Data Scientist for a senior, high-visibility role embedded in their product organization. This is for someone who combines deep statistical expertise with the ability to influence how an entire company makes decisions – not just someone who runs experiments, but someone who builds the culture and infrastructure around them.

What You’ll Be Doing

• Owning and advancing the company’s experimentation roadmap, focused on high-leverage product questions around customer workflows, churn risk, and long-term value
• Designing and analyzing complex A/B tests, multivariate experiments, and Bayesian methods to measure the real impact of product changes
• Applying causal inference techniques (DiD, synthetic control, propensity score matching, instrumental variables) where traditional RCTs aren’t feasible
• Building and governing a unified KPI framework that connects product health metrics to meaningful business outcomes
• Partnering with Data Engineering to build scalable, self-serve experimentation tooling and reusable analytical frameworks
• Translating complex statistical findings into clear, compelling narratives for VP and C-suite audiences
• Mentoring and training junior and mid-level data scientists on experimental design and causal modeling

What We’re Looking For

This is a senior individual contributor role reporting to the Director of Product Data, acting as a strategic thought partner across Product, Engineering, Finance, Design, and Product Marketing. The company is at an inflection point in how it uses data to make product decisions, and this person will be central to shaping that.

Essential:

• 6+ years in applied data science, economics, or product analytics
• Proven expertise in causal inference: DiD, PSM, instrumental variables, quasi-experimentation
• Deep experience in A/B testing methodology including sequential testing, CUPED, variance reduction, and network effects
• Advanced SQL and Python or R for statistical modeling
• Experience with Snowflake or similar cloud data warehouses
• Exceptional communication skills, comfortable presenting to and influencing C-suite stakeholders
• Demonstrated ability to drive change in organizations where experimentation culture is still developing

Nice to have:

• Experience with dbt, Airflow, or Databricks
• Background in SaaS and product data science

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Staff Data Scientist, Finance – Consumption Forecasting

Location: San Francisco or New York | Hybrid (3 days per week in office)

Salary: $240-300k base + bonus + equity (RSUs)

This is a rare chance to own forecasting infrastructure at the center of a high-growth, consumption-based developer platform, one that powers some of the world’s most dynamic applications and scales with every developer and enterprise building on it.

As a consumption-based business, forecasting usage across compute, bandwidth, edge, and storage isn’t a support function. It’s foundational to how we plan infrastructure, revenue, and long-term strategy. This role exists to lead that work at the highest level.

What you’ll own

This is a senior individual contributor role with organization-wide impact. You’ll define forecasting methodology, build systems that scale with a rapidly growing platform, and sit at the intersection of Finance, Infrastructure, Product, and GTM with direct visibility to executive leadership.

• Own production revenue forecasting end-to-end: model development, backtesting, deployment, monitoring, and iteration from first principles to live system
• Build forecasting systems that account for usage-based pricing dynamics, consumption patterns, and customer lifecycle across the platform, built for how this business actually works, not retrofitted SaaS models
• Design hierarchical forecasting models across account, cohort, segment, and global aggregate levels, covering operational, quarterly, and long-range planning cycles
• Establish backtesting, monitoring, and explainability standards that make forecast accuracy transparent and defensible
• Build scenario simulation frameworks to evaluate pricing changes, packaging adjustments, and product launches
• Partner with Finance on board-level reporting, with Infrastructure Engineering on capacity planning, and with Product and GTM on adoption curves and usage drivers
• Set forecasting best practices across the broader Data organization

What we’re looking for

• 7+ years in data science, quantitative analytics, or applied statistics at senior or staff level
• Deep expertise in time-series forecasting and statistical modelling in a usage-based or SaaS environment
• Proven track record building and productionizing ML systems at scale
• Strong Python and SQL, with experience on large-scale usage and billing datasets
• Familiarity with probabilistic modelling, hierarchical forecasting, and causal inference
• Experience partnering with Finance or executive leadership on planning cycles
• Comfortable operating autonomously in fast-moving, ambiguous environments

Nice to have

• Background in cloud infrastructure, developer tools, or consumption-based revenue models
• Familiarity with modern data stacks: Snowflake, Delta Lake, dbt, Airflow
• Prior technical mentorship or informal leadership experience

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Staff GTM Data Scientist

Location: Remote

Salary: $190-220k base

We’re partnering with a fast-growing SaaS company to find a Staff Data Scientist for a senior, high-visibility role sitting at the heart of their product and go-to-market strategy. This isn’t a reporting role or a dashboard-builder position. It’s for someone who can drive a genuine shift toward data-driven decision making across the organization, and who has the technical depth and communication skills to bring leadership along with them.

What You’ll Be Doing

• Owning and advancing the company’s experimentation roadmap, focusing on high-leverage questions around customer workflows, churn risk, and long-term value
• Designing and analyzing complex A/B tests, multivariate experiments, and Bayesian methods to assess the real impact of product and business changes
• Applying causal inference techniques (DiD, synthetic control, propensity score matching, instrumental variables) where traditional RCTs aren’t feasible
• Building and governing a unified KPI framework that connects product health metrics to business outcomes
• Partnering with Data Engineering to build scalable, self-serve experimentation tooling and reusable analytical frameworks
• Translating complex statistical findings into clear, compelling narratives for VP and C-suite audiences
• Mentoring and training junior and mid-level data scientists on experimental design and causal modeling

What We’re Looking For

This is a senior individual contributor role reporting to the Director of GTM Data, acting as a strategic thought partner across Product, Marketing, Finance, and Engineering. The company is at an inflection point in how it uses data, and this person will be central to shaping that.

Essential:

• 6+ years in applied data science, economics, or product analytics
• Proven expertise in causal inference: DiD, PSM, instrumental variables, quasi-experimentation
• Deep experience in A/B testing methodology including sequential testing, CUPED, variance reduction, and network effects
• Advanced SQL and Python or R for statistical modeling
• Experience with Snowflake or similar cloud data warehouses
• Exceptional communication skills, comfortable presenting to and influencing C-suite stakeholders
• Demonstrated ability to drive change in organizations where experimentation culture is still maturing

Nice to have:

• Experience with dbt, Airflow, or Databricks
• Background in SaaS and product data science

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Staff Data Scientist – B2B Marketing & Attribution

Location: Dallas, TX (Onsite, relocation available)

Comp: $180-220k base + equity + bonus

A global semiconductor company is building out its internal marketing and growth analytics function and is seeking a Staff Data Scientist to lead the transformation of its B2B advertising and attribution strategy. This role is highly strategic, focused on evolving measurement capabilities from last-click attribution to a full-funnel, multi-touch approach that better captures the complexity of long sales cycles and account-based engagement. You will play a critical role in integrating audience signals, campaign performance data, and attribution models to drive more efficient customer acquisition and pipeline growth.

What You’ll Do

• Lead the design and implementation of full-funnel attribution models, transitioning from last-click to multi-touch measurement frameworks.

• Develop data-driven strategies to improve CAC, marketing efficiency, and pipeline contribution across B2B channels.

• Integrate and analyze data across platforms including DSPs, CDPs, Google Analytics (GA4), CRM systems, and the enterprise data warehouse.

• Build and scale A/B and multivariate testing frameworks to optimize campaign performance and conversion rates.

• Apply AI/ML techniques to enhance audience segmentation, targeting, and predictive performance modeling.

• Partner with marketing, sales, and product stakeholders to translate business goals into measurable analytics initiatives.

• Establish best practices around marketing measurement, experimentation, and attribution in a complex, account-based environment.

What You Bring

• 8+ years of experience in data science, with a strong emphasis on B2B marketing, advertising, or growth analytics.

• Proven experience advancing organizations from last-click to multi-touch attribution models.

• Strong programming skills in Python and SQL, with experience working in modern data warehouse environments (e.g., Snowflake, BigQuery, Redshift).

• Hands-on experience with Google Analytics, DSPs, CDPs, and ABM platforms such as 6sense or Demandbase.

• Deep understanding of CAC, funnel metrics, pipeline attribution, and campaign performance analysis.

• Experience leading experimentation strategies and applying statistical methods to measure impact.

• Ability to communicate complex analytical insights to cross-functional and executive stakeholders.

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