Staff Data Scientist, Forecasting

Jobtailor; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Jobtailor in San Francisco seeks a technical lead for the forecasting team to own strategy and implementation of time-series models forecasting key metrics like MAU, delivering accurate, interpretable forecasts at scale.

You will lead end-to-end modeling lifecycle, define architectures, partner with Engineering and BizOps, mentor at least two data scientists, and present insights to senior leadership.

Qualifications

• 8+ years of post-graduate academic and industry experience in time-series/forecasting with web-scale data.
• Bachelor’s degree in Computer Science or equivalent.
• Track record of deliverable, calibrated, explainable forecasting systems for decision-making.
• Strong background in time-series modeling and applied statistics/econometrics; MS/PhD preferred.
• Proficiency in Python and SQL; experience building reliable data pipelines.
• Business acumen with ownership mindset; ability to connect outputs to levers.
• Excellent communication for executive audiences; proven technical leadership.

Responsibilities

• Be the technical lead for the forecasting team.
• Own strategy and implementation of forecasting models for key metrics like MAU.
• Lead full modeling lifecycle end-to-end: framing, engineering, development, experimentation, deployment, monitoring, and explainability.
• Define architectures and standards; partner with Engineering to scale training/inference.
• Translate forecasts into decisions; present outputs and scenarios to VP-level leadership.
• Drive time-series impact beyond forecasts, including anomaly detection and causal analyses.
• Embed forecasting into business rhythms with BizOps/Finance and product teams.
• Mentor and develop at least two data scientists, raising technical quality and impact.

Skills

Time-series modeling

Python

SQL (Hive/Presto/Spark SQL)

Data pipelines (Airflow)

Executive communication

Technical leadership

Experimentation/backtesting

Forecasting explainability

Education

Bachelor's degree in CS or related field

MS/PhD preferred

Tools

Airflow

Hive/Presto/Spark SQL

Job description

Responsibilities

• Be the technical lead for the forecasting team.
• Own the strategy and implementation of forecasting models of key company metrics (e.g., monthly active users), delivering accurate, interpretable forecasts at scale.
• Lead the full modeling lifecycle end to end: problem framing, feature engineering, model development and prototyping, experimentation and backtesting, deployment, monitoring/drift detection, and explainability.
• Set the forecasting technical vision. Define model architectures and standards, and partner with Engineering to shape the forecasting platform for efficient training/inference today and the scalability needed for the next generation of models.
• Translate forecasts into decisions. Present outputs, scenario analyses, and recommendation frameworks to senior leadership with clarity and brevity. This is a high‑visibility role with regular VP-level exposure.
• Drive broader time‑series impact beyond point forecasts—e.g., anomaly detection, automated root‑cause analysis, campaign/channel attribution, and early‑warning signals for business health.
• Embed forecasting into the business. Partner with BizOps/Finance and product teams to integrate forecasts and insights into operational rhythms, executive decision‑making, and strategic planning.
• Lead and mentor. Guide the work of at least two data scientists, raising the bar on technical quality, execution, and impact through candid, continuous feedback and coaching.

Requirements

• 8+ years of combined post‑graduate academic and industry experience building and shipping production time‑series/forecasting models with web‑scale data.
• Bachelor’s degree in a relevant field such as Computer Science or equivalent experience.
• A track record of delivering adjustable, well‑calibrated, and explainable forecasting systems that informing decision‑making.
• Strong background in time‑series modeling and applied statistics/econometrics; advanced degree (MS or PhD) preferred.
• Expertise in at least one scripting language (ideally Python).
• Strong SQL skills (Hive/Presto/Spark SQL) and experience building reliable data pipelines/workflows (e.g., Airflow).
• Business acumen and ownership mindset—able to simplify complex problems, connect model outputs to business levers, and prioritize for impact.
• Excellent communication skills—able to distill complex analyses and uncertainty into concise narratives for executive audiences.
• Proven technical leadership—success leading critical projects and materially influencing the scope and output of other contributors.