Staff Data Scientist, Forecasting
Staff Data Scientist, Forecasting
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
- 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
Qualifications
- 8+ years of combined post-graduate academic and industry experience building and shipping production time-series/forecasting models with web-scale data
- A track record of delivering adjustable, well-calibrated, and explainable forecasting systems that inform 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
Locations
- San Francisco, CA, US; Remote, US (Remote)
Salary
164,695 - 339,078 USD / yearly
Estimated Salary Rangemedium confidence
180,000 - 250,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- time-series modelingintermediate
- applied statistics/econometricsintermediate
- Python scriptingintermediate
- SQL (Hive/Presto/Spark SQL)intermediate
- building data pipelines/workflows (e.g., Airflow)intermediate
- business acumenintermediate
- communication skillsintermediate
- technical leadershipintermediate
Required Qualifications
- 8+ years of combined post-graduate academic and industry experience building and shipping production time-series/forecasting models with web-scale data (experience)
- A track record of delivering adjustable, well-calibrated, and explainable forecasting systems that inform decision-making (experience)
- Strong background in time-series modeling and applied statistics/econometrics; advanced degree (MS or PhD) preferred (experience)
- Expertise in at least one scripting language (ideally Python) (experience)
- Strong SQL skills (Hive/Presto/Spark SQL) and experience building reliable data pipelines/workflows (e.g., Airflow) (experience)
- Business acumen and ownership mindset—able to simplify complex problems, connect model outputs to business levers, and prioritize for impact (experience)
- Excellent communication skills—able to distill complex analyses and uncertainty into concise narratives for executive audiences (experience)
- Proven technical leadership—success leading critical projects and materially influencing the scope and output of other contributors (experience)
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
- 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
Tags & Categories
Staff Data Scientist, Forecasting
Staff Data Scientist, Forecasting
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
- 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
Qualifications
- 8+ years of combined post-graduate academic and industry experience building and shipping production time-series/forecasting models with web-scale data
- A track record of delivering adjustable, well-calibrated, and explainable forecasting systems that inform 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
Locations
- San Francisco, CA, US; Remote, US (Remote)
Salary
164,695 - 339,078 USD / yearly
Estimated Salary Rangemedium confidence
180,000 - 250,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- time-series modelingintermediate
- applied statistics/econometricsintermediate
- Python scriptingintermediate
- SQL (Hive/Presto/Spark SQL)intermediate
- building data pipelines/workflows (e.g., Airflow)intermediate
- business acumenintermediate
- communication skillsintermediate
- technical leadershipintermediate
Required Qualifications
- 8+ years of combined post-graduate academic and industry experience building and shipping production time-series/forecasting models with web-scale data (experience)
- A track record of delivering adjustable, well-calibrated, and explainable forecasting systems that inform decision-making (experience)
- Strong background in time-series modeling and applied statistics/econometrics; advanced degree (MS or PhD) preferred (experience)
- Expertise in at least one scripting language (ideally Python) (experience)
- Strong SQL skills (Hive/Presto/Spark SQL) and experience building reliable data pipelines/workflows (e.g., Airflow) (experience)
- Business acumen and ownership mindset—able to simplify complex problems, connect model outputs to business levers, and prioritize for impact (experience)
- Excellent communication skills—able to distill complex analyses and uncertainty into concise narratives for executive audiences (experience)
- Proven technical leadership—success leading critical projects and materially influencing the scope and output of other contributors (experience)
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
- 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
Tags & Categories