To Apply for this Job Click Here

Lead Data Scientist

Location: Hybrid (Tempe, AZ preferred; Dallas, TX or Atlanta, GA also considered)

Schedule: 4 days onsite per week (Monday-Thursday)

Compensation: $130-145,000 base salary + bonus eligibility

Overview

A leading organization is seeking a Lead Data Scientist to drive advanced risk and financial modeling initiatives that directly influence business performance. This is a highly visible individual contributor role for a senior-level data scientist who can independently lead complex projects, quantify business impact, and effectively communicate insights to executive stakeholders.

This team is responsible for identifying significant financial opportunities, developing predictive models, and delivering measurable outcomes. Success in this role requires strong technical expertise, business acumen, and the ability to defend models in regulated environments.

Responsibilities

• Lead end-to-end data science projects from problem definition through implementation.
• Develop, validate, and monitor predictive and risk models using large-scale financial and customer datasets.
• Apply advanced statistical and machine learning techniques, including gradient boosting models.
• Translate model outputs into actionable business recommendations and financial impact.
• Present findings, model performance, and recommendations to senior leadership and executive stakeholders.
• Support model governance activities, audits, and validation reviews.
• Mentor and support team members while serving as a technical leader within the organization.

Required Qualifications

• 8+ years of experience in data science, quantitative analytics, or predictive modeling.
• Strong background in finance, credit, or risk modeling within a regulated industry.
• Advanced Python and SQL skills.
• Strong foundation in mathematics, statistics, and predictive modeling.
• Experience building and validating models used for financial or risk-related decision making.
• Experience presenting technical concepts and model results to executive audiences.
• Proven ability to independently own and deliver complex projects.
• Experience supporting external audits and defending analytical models.

Preferred Experience

Experience in one or more of the following areas:

• Credit risk modeling
• Probability of Default (PD) modeling
• Credit loss or loss forecasting models
• Scorecard development
• Payment behavior analytics
• Banking, lending, mortgage, credit card, or credit bureau data
• Financial impact and portfolio analysis

Education

• Bachelor’s degree in Mathematics, Statistics, Economics, Computer Science, Engineering, or another quantitative discipline required.
• Master’s degree in Data Science, Statistics, Mathematics, Economics, Engineering, or a related quantitative field preferred.

Why Join?

• Direct ownership of high-value analytics initiatives.
• Opportunity to see models implemented and tied to measurable business outcomes.
• Significant executive exposure and influence.
• High-impact, collaborative team where individual contributions are highly visible.
• Ability to drive projects that identify and deliver multi-million-dollar business opportunities.

To Apply for this Job Click Here

To Apply for this Job Click Here

Lead Data Scientist

We are looking for a Lead Data Scientist to own complex quantitative and risk modeling projects from problem definition through implementation. This is a highly visible role where your models will directly influence multi-million-dollar business opportunities and be presented to executive stakeholders.

What You’ll Do

• Build, evaluate, and validate quantitative and risk models.
• Work with large financial and credit datasets.
• Develop models using Python, SQL, and gradient boosting techniques.
• Translate model outputs into measurable financial and dollar impact.
• Own projects end-to-end, from defining the problem through implementation.
• Present technical findings and recommendations to senior and executive stakeholders.
• Defend models and methodology during external audits.
• Help identify and quantify significant financial opportunities.
• Provide technical guidance and support to other team members.

What We’re Looking For

• 8+ years of data science/modeling experience
• Strong Python and SQL skills.
• Strong data modeling experience.
• Strong mathematical and statistical foundation.
• Experience with gradient boosting models.
• Proven ability to independently lead projects end-to-end.
• Excellent communication and presentation skills.
• Comfortable explaining technical work and model results to executives.
• Experience working through external audits and defending models.
• Finance/risk modeling experience within a regulated industry is required.

Required Domain Experience

You should have experience in areas such as:

• Credit risk modeling
• Financial or loss modeling
• Probability of Default (PD) modeling
• Credit scorecards
• Payment behavior modeling
• Credit attribute modeling
• Mortgage, credit card, banking, or credit bureau data
• Risk modeling within a regulated financial institution

Education

• Bachelor’s degree in Mathematics, Statistics, Economics, Computer Science, Quantitative Engineering, or another highly quantitative field.
• Master’s degree in Data Science or a related quantitative discipline preferred.

The Ideal Candidate

This role is suited to a senior-level Data Scientist who can operate independently, drive complex projects, and communicate confidently with both technical and executive audiences.

Location: Tempe, AZ preferred; Dallas or Atlanta also considered
Working Model: Hybrid, 4 days per week (Monday-Thursday)
Compensation: $140,000-$150,000 base
Visa: U.S. Citizen or Green Card holder only

To Apply for this Job Click Here

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 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

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

Lead Data Scientist

$140,000-$150,000 + 10% bonus

Overview

A growing organisation is seeking an experienced Lead Data Scientist to drive strategic analytics initiatives across customer experience, operational efficiency, asset performance, and cost optimisation. This highly visible role partners closely with senior business leaders to identify challenges, develop data-driven solutions, and influence decision-making through advanced analytics and predictive modelling.
The ideal candidate combines deep technical expertise with strong business acumen, excels at communicating complex concepts to non-technical audiences, and has a proven track record of leading projects from concept through implementation with minimal supervision.

Key Responsibilities

Project Leadership & Stakeholder Management

• Build trusted relationships with senior stakeholders, including director- and executive-level leaders.
• Independently lead projects from problem definition through deployment and performance monitoring.
• Facilitate intake sessions, define project scope, estimate effort, manage priorities, and communicate progress effectively.
• Mentor and guide junior analysts and data scientists on methodology, project structure, and technical execution.
• Present findings and recommendations to leadership teams, including executive audiences.

Advanced Analytics & Data Science

• Develop and deploy predictive models using machine learning techniques such as gradient boosting, ensemble methods, decision trees, and other supervised and unsupervised learning approaches.
• Design segmentation frameworks using clustering and classification techniques.
• Conduct statistical analysis, model validation, back-testing, and performance measurement to ensure reliability and business value.
• Identify patterns, root causes, and opportunities through large-scale data analysis.
• Develop innovative analytical approaches to improve insight generation and operational effectiveness.
• Support business decision-making through forecasting, scoring models, optimisation techniques, and operational analytics.

Data Management & Process Improvement

• Gather, reconcile, clean, validate, and integrate data from multiple internal and external sources.
• Establish data quality standards and controls to improve accuracy and consistency.
• Design and implement processes that reduce risk, improve efficiency, and create measurable business value.
• Monitor and assess the ongoing performance of implemented analytical solutions.

Executive Communication

• Translate complex analytical findings into actionable business recommendations.
• Develop compelling executive presentations and data-driven narratives that influence organisational strategy and operational improvements.

Required Qualifications

• Bachelor’s degree in a quantitative field such as Mathematics, Statistics, Engineering, Economics, Computer Science, or a related discipline.
• 10+ years of experience in data science, advanced analytics, predictive modelling, risk analytics, or a related quantitative function.
• 3+ years of hands-on Python experience building machine learning, forecasting, regression, classification, and segmentation models.
• 3+ years of advanced SQL experience, including optimisation of complex queries across large datasets.
• Strong expertise in statistical methods, probability theory, experimental design, predictive analytics, sampling techniques, and model evaluation.
• Demonstrated experience analysing large structured and unstructured datasets.
• Experience presenting analytical insights to executive audiences and influencing strategic decisions.
• Ability to communicate complex technical concepts to business stakeholders with varying levels of analytical expertise.
• Strong project management, prioritisation, and multitasking skills.
• Proven ability to work independently in a fast-paced, evolving environment.
• Experience sourcing, validating, and integrating data from multiple systems and platforms.

Preferred Qualifications

• Master’s degree in a quantitative discipline.
• Experience in risk management, portfolio analytics, financial modelling, or asset performance analysis.
• Experience using consumer, credit, demographic, or third-party external datasets.
• Prior experience developing scoring models, predictive risk models, or decision-support frameworks.
• Experience within highly regulated industries where model governance, validation, and audit requirements are critical.
• Familiarity with model risk management frameworks and third-party model review processes.
• Experience working with cloud-based data and analytics environments, including object storage and large-scale compute platforms.
• Experience with modern data warehouse technologies.

To Apply for this Job Click Here

To Apply for this Job Click Here

Lead Data Scientist

$140,000-$150,000 + 10% bonus

Overview

A growing organisation is seeking an experienced Lead Data Scientist to drive strategic analytics initiatives across customer experience, operational efficiency, asset performance, and cost optimisation. This highly visible role partners closely with senior business leaders to identify challenges, develop data-driven solutions, and influence decision-making through advanced analytics and predictive modelling.
The ideal candidate combines deep technical expertise with strong business acumen, excels at communicating complex concepts to non-technical audiences, and has a proven track record of leading projects from concept through implementation with minimal supervision.

Key Responsibilities

Project Leadership & Stakeholder Management

• Build trusted relationships with senior stakeholders, including director- and executive-level leaders.
• Independently lead projects from problem definition through deployment and performance monitoring.
• Facilitate intake sessions, define project scope, estimate effort, manage priorities, and communicate progress effectively.
• Mentor and guide junior analysts and data scientists on methodology, project structure, and technical execution.
• Present findings and recommendations to leadership teams, including executive audiences.

Advanced Analytics & Data Science

• Develop and deploy predictive models using machine learning techniques such as gradient boosting, ensemble methods, decision trees, and other supervised and unsupervised learning approaches.
• Design segmentation frameworks using clustering and classification techniques.
• Conduct statistical analysis, model validation, back-testing, and performance measurement to ensure reliability and business value.
• Identify patterns, root causes, and opportunities through large-scale data analysis.
• Develop innovative analytical approaches to improve insight generation and operational effectiveness.
• Support business decision-making through forecasting, scoring models, optimisation techniques, and operational analytics.

Data Management & Process Improvement

• Gather, reconcile, clean, validate, and integrate data from multiple internal and external sources.
• Establish data quality standards and controls to improve accuracy and consistency.
• Design and implement processes that reduce risk, improve efficiency, and create measurable business value.
• Monitor and assess the ongoing performance of implemented analytical solutions.

Executive Communication

• Translate complex analytical findings into actionable business recommendations.
• Develop compelling executive presentations and data-driven narratives that influence organisational strategy and operational improvements.

Required Qualifications

• Bachelor’s degree in a quantitative field such as Mathematics, Statistics, Engineering, Economics, Computer Science, or a related discipline.
• 10+ years of experience in data science, advanced analytics, predictive modelling, risk analytics, or a related quantitative function.
• 3+ years of hands-on Python experience building machine learning, forecasting, regression, classification, and segmentation models.
• 3+ years of advanced SQL experience, including optimisation of complex queries across large datasets.
• Strong expertise in statistical methods, probability theory, experimental design, predictive analytics, sampling techniques, and model evaluation.
• Demonstrated experience analysing large structured and unstructured datasets.
• Experience presenting analytical insights to executive audiences and influencing strategic decisions.
• Ability to communicate complex technical concepts to business stakeholders with varying levels of analytical expertise.
• Strong project management, prioritisation, and multitasking skills.
• Proven ability to work independently in a fast-paced, evolving environment.
• Experience sourcing, validating, and integrating data from multiple systems and platforms.

Preferred Qualifications

• Master’s degree in a quantitative discipline.
• Experience in risk management, portfolio analytics, financial modelling, or asset performance analysis.
• Experience using consumer, credit, demographic, or third-party external datasets.
• Prior experience developing scoring models, predictive risk models, or decision-support frameworks.
• Experience within highly regulated industries where model governance, validation, and audit requirements are critical.
• Familiarity with model risk management frameworks and third-party model review processes.
• Experience working with cloud-based data and analytics environments, including object storage and large-scale compute platforms.
• Experience with modern data warehouse technologies.

To Apply for this Job Click Here