Lead Product Data Scientist, Forecasting

Description

The product data science team at the company turns complex data into insights that guide product strategy, financial planning, and long-term growth. The team works closely with Product, Engineering, Finance, and Go-to-Market teams.

The company is scaling its platform, widening its product range, and adding AI-powered features, which makes accurate forecasts and an understanding of what drives them more important than ever.

The team pairs statistical modeling and analytics with a strong grasp of the business, and it aims to embed data science in the product development lifecycle so that investment and opportunity decisions move faster.

This is a senior, hands-on technical leadership role where you will own and advance the company's forecasting work and shape product and strategic decisions. You will sit where data science, product strategy, and business performance meet.

The job goes beyond building accurate models or reporting past trends. You will test assumptions, dig into surprising results, and turn findings into clear recommendations.

Responsibilities

• Own revenue forecasting from problem framing and data preparation through modeling, evaluation, deployment, and ongoing monitoring, so that forecasts are trusted inputs to planning and product decisions.
• Build forecasting models across different time horizons and levels of detail using hierarchical, probabilistic and Bayesian, classical time series, machine learning, and new time series foundation model methods.
• Set the company's forecasting standards by defining how accuracy, bias, calibration, and uncertainty are measured, and create consistent evaluation practices.
• Lead the technical direction of forecasting by assessing modeling options, reviewing designs, mentoring data scientists, and raising the bar for statistical methods, code quality, and analytical rigor.
• Work with Product, Finance, Engineering, and Go-to-Market teams on scenario planning and strategic investment analysis, and turn vague business questions into concrete analysis plans.
• Uncover what drives customer and product performance using advanced research such as mixed effects models, clustering, and time series analysis.
• Lead root cause investigations when actual results diverge from forecasts, determining whether the cause is data quality, model limits, customer behavior, product or pricing changes, or external factors.
• Create repeatable root cause frameworks, templates, and dashboards so data scientists and business stakeholders can investigate performance consistently.
• Build self-service forecasting and analytics tools that let product managers and cross-functional leaders explore trends, test scenarios, and decide on their own.
• Partner with Data Engineering to build reliable pipelines and dbt models that deliver clean, well-structured data for forecasting and downstream analysis.
• Assess emerging AI and forecasting technologies and bring in new modeling methods, foundation models, and AI tools that improve accuracy, efficiency, and decision support.
• Explain complex findings and their business impact clearly to audiences from individual contributors to senior executives.

Requirements

• You have 10+ years in data science, product analytics, applied statistics, or a related quantitative field, including substantial hands-on experience deploying forecasting solutions in production.
• You have deep knowledge of time series forecasting, statistical modeling, and model evaluation, and have applied hierarchical forecasting, probabilistic or Bayesian methods, and machine learning to complex business problems.
• You are highly proficient in SQL and in Python or R, have built production-quality analytical solutions, and are familiar with dbt and modern analytics engineering.
• You have led technical work by setting direction on complex projects, mentoring data scientists, reviewing modeling approaches, and shaping standards across teams.
• You combine statistical rigor with business intuition and understand how product work, customer behavior, pricing, and strategic investment affect financial and business results.
• You can take a complex analytical problem from early exploration through production, monitoring, and continuous improvement.
• You can turn ambiguous business questions into well-defined analyses, set meaningful success metrics, and deliver recommendations people act on.
• You explain advanced statistical ideas and modeling tradeoffs well to technical and non-technical audiences, including senior executives.
• You work well across functions with Product, Finance, Engineering, and business leaders to influence strategy and improve customer outcomes.
• You are comfortable with ambiguity, move quickly, and balance analytical rigor with the speed and iteration a high-growth product environment requires.
• You have an AI-first mindset and are curious about and familiar with time series foundation models, LLMs, and agentic tools that improve forecasting, analytical workflows, and decisions.
• You hold a degree in Statistics, Mathematics, Computer Science, Economics, Finance, Engineering, or another quantitative field, or an MBA with a quantitative focus. Equivalent hands-on experience in applied forecasting or statistical modeling is equally valued.
• Experience building and scaling forecasting in a high-growth B2B SaaS or product-led technology company.
• Experience with revenue forecasting, financial modeling, scenario planning, or business performance analysis in a subscription business.
• Familiarity with cloud data platforms, distributed processing, and modern data engineering infrastructure.
• Experience applying mixed effects models, clustering, and probabilistic modeling to customer behavior and product performance.
• Experience building self-service analytics tools, dashboards, or forecasting platforms for cross-functional business users.
• Hands-on exposure to time series foundation models, LLMs, or agentic AI for forecasting and decision support.
• Experience setting up forecasting governance, model evaluation frameworks, or standard root cause analysis practices across an organization.

Conditions

This role is based in the United States, with a base salary range of $172,000 to $258,000 USD across US locations. Final pay depends on skills, experience, education or training, and location.

Up to 10% travel may be required for onboarding, client or partner work where relevant, team meetings, and industry events. Travel is arranged in advance.

Your recruiter can share the salary range for your preferred location during hiring.

The company is an equal opportunity employer that does not discriminate on race, ethnicity, citizenship, or other protected characteristics, and it is unlawful in Massachusetts to require a lie detector test as a condition of employment.

Benefits

• Possible participation in the annual cash bonus plan, equity, and sign-on payments.
• Health, welfare, and wellbeing benefits, subject to eligibility.
• Variable compensation (OTE) applies to sales and customer success roles, not this role.