Lead Data Scientist-Retail Demand Forecasting

MathCo; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

MathCo is seeking a Lead Data Scientist to drive forecasting, optimization, and applied ML initiatives, leading the technical direction from framing problems to deployment in the cloud.

You'll work across the lifecycle, solving business problems with time-series and ML models, and partnering with stakeholders to deliver scalable, production-ready analytics in GCP and Azure environments.

Qualifications

• Proven leadership of data science initiatives.
• Ability to translate business problems into analytical solutions.
• Strong experience with time-series forecasting.
• Experience delivering production ML solutions with CI/CD.

Responsibilities

• Lead the design and development of forecasting and optimization solutions for complex business problems.
• Translate business requirements into well-defined data science problems and measurable outcomes.
• Develop and evaluate forecasting models using time-series and ML techniques.
• Apply optimization to turn forecasts into actionable decisions.
• Perform hands-on development with Python and SQL, including feature engineering and model validation.
• Design cloud-native solutions, leveraging GCP and Azure, with scalable ML/analytics pipelines.
• Collaborate with stakeholders, data engineers, and teammates to deliver end-to-end solutions.
• Provide technical direction and establish sound modeling and engineering practices.

Skills

Data science

Forecasting

Optimization

Python

SQL

Cloud platforms (GCP/Azure)

CI/CD practices

Stakeholder communication

Tools

Cloud Run

GKE

Cloud Functions

APIs

ML pipelines

Job description

MathCo is looking for a Lead Data Scientist with deep experience in forecasting, optimization, and applied machine learning to lead the development of advanced analytical solutions for complex business problems.

This role is designed for someone who can combine strong hands‑on data science capabilities with technical leadership. You will work across the lifecycle of a solution, from framing ambiguous business problems and designing analytical approaches to developing models, evaluating their performance, and supporting deployment in a cloud environment.

Experience solving forecasting problems within fashion, retail, consumer, CPG, demand planning, or other B2C environments will be particularly relevant.

What You’ll Do

• Lead the design and development of forecasting and optimization solutions addressing complex business and operational problems.
• Translate business requirements into well‑defined data science problems, analytical approaches, and measurable outcomes.
• Develop and evaluate forecasting models using appropriate time‑series, statistical, and machine‑learning techniques.
• Apply optimization approaches to translate forecasts and model outputs into actionable business decisions.
• Perform hands‑on development using Python and SQL, including data exploration, feature engineering, model development, validation, and performance evaluation.
• Design solutions capable of operating within modern cloud environments, particularly Google Cloud Platform (GCP) and Azure.
• Work with GCP services and infrastructure such as Cloud Run, Google Kubernetes Engine (GKE), Cloud Functions, and APIs.
• Partner closely with business stakeholders, data scientists, data engineers, and other technical teams to move solutions from problem definition through implementation.
• Provide technical direction, review analytical approaches, and help establish sound modeling and engineering practices across the workstream.
• Support productionization through CI/CD and scalable ML/analytics engineering practices.
• Clearly communicate model assumptions, methodology, limitations, results, and recommendations to technical and business stakeholders.

What We’re Looking For

• Strong professional experience in data science and applied machine learning, including ownership of complex analytical initiatives.
• Demonstrated hands‑on experience building and evaluating forecasting models.
• Experience with optimization techniques and applying model outputs to business decision‑making.
• Strong proficiency in Python and SQL.
• Hands‑on experience with cloud‑based data science or ML workloads, with GCP experience strongly preferred.
• Experience working with APIs, cloud‑native services, production ML workflows, and CI/CD practices.
• Ability to independently structure ambiguous problems and provide technical leadership from solution design through delivery.
• Strong stakeholder communication skills and the ability to explain sophisticated analytical concepts to both technical and non‑technical audiences.
• Experience working within fashion retail, B2C retail, CPG, demand forecasting, inventory, merchandising, supply chain, or an adjacent consumer domain is highly desirable.

Nice to Have

• Experience with Azure in addition to GCP.
• Experience with MCP interfaces.
• Familiarity with Atlan or similar metadata/catalog and data‑governance platforms.
• Experience designing forecasting solutions that integrate with downstream optimization or decision‑support systems.
• Consulting experience delivering analytical solutions in a client‑facing environment.