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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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To Apply for this Job Click Here

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

To Apply for this Job Click Here

To Apply for this Job Click Here

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

To Apply for this Job Click Here

To Apply for this Job Click Here

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.

To Apply for this Job Click Here

To Apply for this Job Click Here

Data Scientist

Liverpool area (3 days onsite) | £45,000 to £50,000

This is a strong opportunity for a commercially minded Data Scientist to step into a growing team at a key point in its data journey. You will have the chance to work on high-impact customer projects while helping shape how data science is delivered and scaled across the business.

The Company

They are a well-established UK retail organisation investing heavily in building a modern data and AI capability. The business is transitioning away from ad hoc analytics towards a more structured and scalable data science approach. A major focus is on customer insight and personalisation, driven by a new loyalty programme. The culture is collaborative, pragmatic, and focused on delivering tangible value.

The Role

You will work closely with the Data Science Lead and a small, high-impact team to deliver production-ready models and insights.

• Build and deploy machine learning models to support customer and commercial decision making
• Deliver key use cases such as customer segmentation, lifetime value modelling, and demand forecasting
• Work across the full data science lifecycle from problem definition through to deployment
• Contribute to the development of a new data science platform
• Collaborate with data engineers, analysts, and business stakeholders to deliver measurable impact
• Communicate complex findings clearly and confidently to non-technical audiences
• Support scaling existing models into broader business use

Your Skills and Experience

• Strong commercial experience applying data science and machine learning to business problems
• Proficiency in Python preferred, or R with openness to transition
• Solid SQL skills and experience working with complex datasets
• Experience delivering end-to-end data science solutions, including production deployment
• Exposure to cloud platforms such as GCP or Azure is beneficial
• Strong stakeholder management and communication skills
• A practical, outcome-focused approach to problem solving

What They Offer

• £45,000 to £50,000 salary
• Hybrid working with 3 days onsite in the Liverpool area
• The opportunity to shape and influence a growing data science function
• Clear progression as the team scales
• Collaborative and inclusive working environment
• Benefits including staff discount, wellbeing support, pension, and development opportunities

How to Apply

Apply now with your CV to explore this opportunity further.

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To Apply for this Job Click Here

Data Scientist

Knowsley – 3 days a week

The Company
They are a well established UK retail business undergoing a broader data and AI transformation. The organisation is investing in building a modern Data Science capability to support decision making across commercial and customer functions. Their culture is practical, collaborative and delivery focused, with an emphasis on creating value quickly rather than over engineering solutions. Data is increasingly central to their strategy, particularly with the launch of a new customer loyalty programme.

The Role
You will work closely with the Data Science Lead and a small but growing team to develop and productionise machine learning solutions.

• Build and deploy models that support customer and commercial decision making
• Work on use cases such as customer segmentation, lifetime value modelling and demand forecasting
• Contribute to the development of a scalable Data Science platform
• Collaborate with data engineers, analysts and business stakeholders
• Translate business problems into data science solutions and clearly communicate outputs
• Support the expansion of existing models into broader production use

Your Skills and Experience

• Strong commercial experience in Data Science using Python
• Solid SQL skills and experience working with large datasets
• End to end experience delivering Data Science solutions from problem definition through to deployment
• Experience building and validating machine learning models
• Ability to communicate insights clearly to non technical audiences
• A practical, business focused approach to problem solving

What They Offer

• Hybrid working with three days per week in Knowsley
• Opportunity to shape and influence a growing Data Science function
• Exposure to end to end project work with real business impact
• A collaborative environment focused on learning and delivery

How to Apply
Apply now to learn more about this Data Scientist opportunity and the impact you can make within a growing data driven environment.

To Apply for this Job Click Here

To Apply for this Job Click Here

Data Scientist – Merseyside

As a Data Scientist, you will work closely with the Lead Data Science and cross-functional teams to develop and deploy models that support customer and commercial decision-making. Responsibilities include:

• Building and deploying machine learning models across customer use cases
• Delivering projects focused on customer segmentation, lifetime value, and forecasting
• Supporting targeted marketing activity, including campaigns and personalisation
• Collaborating with data engineers, analysts, and business stakeholders
• Contributing to the development of a scalable Data Science platform
• Ensuring models are production-ready and drive measurable business impact

Your Skills & Experience

• Strong commercial experience in Data Science, delivering end-to-end projects
• Proficiency in Python, with solid SQL skills
• Experience applying machine learning and statistical techniques to business problems
• Exposure to deploying models into production environments
• Understanding of cloud platforms such as GCP or Azure is beneficial
• Ability to communicate insights clearly to non-technical stakeholders
• A practical, delivery-focused mindset with a collaborative approach

To Apply for this Job Click Here