Requirements
A data scientist with a passion for building and deploying models that solve real-world pricing and risk challenges (Desirable) Exposure to time-series forecasting and advanced ML methods Thinks in Probabilities: You’re comfortable treating forecasts as distributions rather than point estimates, using scenario analysis and Monte Carlo simulations to capture uncertainty (Desirable) Startup or entrepreneurial background: you’re used to ambiguous environments and can set your own course Builds for Impact: You thrive on translating complex analytics into actionable outputs, ensuring your models have a direct influence on key business decisions (Desirable) Experience building portfolio-level risk models or complex pricing engines in a production environment Enjoys End-to-End Ownership: From data exploration and feature engineering to deployment and monitoring, you embrace the full model lifecycle (Desirable) Familiarity with energy markets is a plus, but not mandatory Hands-On Experience: You’ve applied advanced analytics or quantitative methods in industry (finance, commodities, logistics, etc.) particularly around pricing or risk Proficient in Python & Cloud: You can confidently code in Python and are comfortable working in a cloud environment Thrives in a High-Trust Environment: You have a proactive mindset, taking initiative to learn, iterate, and deliver results independently What the job involves
Data is at the heart of our core product, RED (Renewable Energy Direct). As our second Data Scientist, you’ll work on RED's high-impact problems and directly influence the future of renewable energy Join as our second data scientist and help shape the team's future data tech stack and contribute to the development of a world-class data science foundation Primarily developing and maintaining statistical and machine learning models that compute risk margins and optimized pricing for our customers Incorporate probabilistic frameworks that account for volume and market-price uncertainties, ensuring robust margins under different scenarios Work on time-series forecasting models for energy consumption/generation and underlying market prices Aggregate and model diverse data streams (existing contracts, pipeline deals) at varying horizons (hourly to yearly) to inform portfolio decisions Research & Prototyping: Experiment with new algorithms or modeling techniques (e.g., scenario analysis, optimization frameworks) Owning the full machine learning lifecycle, from research and development to deployment and monitoring Current Tech Stack: AWS (Lambda, API Gateway), Python, SQL, MLFlow, Postgres (RDS), dbt, Quicksight, Terraform/Terragrunt
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A data scientist with a passion for building and deploying models that solve real-world pricing and risk challenges (Desirable) Exposure to time-series forecasting and advanced ML methods Thinks in Probabilities: You’re comfortable treating forecasts as distributions rather than point estimates, using scenario analysis and Monte Carlo simulations to capture uncertainty (Desirable) Startup or entrepreneurial background: you’re used to ambiguous environments and can set your own course Builds for Impact: You thrive on translating complex analytics into actionable outputs, ensuring your models have a direct influence on key business decisions (Desirable) Experience building portfolio-level risk models or complex pricing engines in a production environment Enjoys End-to-End Ownership: From data exploration and feature engineering to deployment and monitoring, you embrace the full model lifecycle (Desirable) Familiarity with energy markets is a plus, but not mandatory Hands-On Experience: You’ve applied advanced analytics or quantitative methods in industry (finance, commodities, logistics, etc.) particularly around pricing or risk Proficient in Python & Cloud: You can confidently code in Python and are comfortable working in a cloud environment Thrives in a High-Trust Environment: You have a proactive mindset, taking initiative to learn, iterate, and deliver results independently What the job involves
Data is at the heart of our core product, RED (Renewable Energy Direct). As our second Data Scientist, you’ll work on RED's high-impact problems and directly influence the future of renewable energy Join as our second data scientist and help shape the team's future data tech stack and contribute to the development of a world-class data science foundation Primarily developing and maintaining statistical and machine learning models that compute risk margins and optimized pricing for our customers Incorporate probabilistic frameworks that account for volume and market-price uncertainties, ensuring robust margins under different scenarios Work on time-series forecasting models for energy consumption/generation and underlying market prices Aggregate and model diverse data streams (existing contracts, pipeline deals) at varying horizons (hourly to yearly) to inform portfolio decisions Research & Prototyping: Experiment with new algorithms or modeling techniques (e.g., scenario analysis, optimization frameworks) Owning the full machine learning lifecycle, from research and development to deployment and monitoring Current Tech Stack: AWS (Lambda, API Gateway), Python, SQL, MLFlow, Postgres (RDS), dbt, Quicksight, Terraform/Terragrunt
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