Forecasting Lead: Retail Demand & Optimization

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 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.