Data Scientist II
Job description
About the role
The role partners with Forecast Analysts, Product, and Engineering to ensure models remain accurate, reliable, and usable in production. It bridges data exploration and robust operational systems for power market analytics.
Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.
Key facts
What you'll do
Python libraries and data pipelines are built and scaled to support extraction, feature engineering, and supervised learning for forecasting workflows.
Daily forecasting operations are sustained through runtime monitoring, exception handling, and model configuration to uphold service reliability.
Research models are transitioned into production systems in collaboration with Engineering to ensure robust handoff and operational integrity.
Moderately complex problems are tackled independently by analyzing data patterns to identify root causes and drive toward solutions.
Model accuracy within a forecasting SaaS environment is improved through research and development on data patterns and evolving data inputs.
Requirements
3+ years of professional experience in data science focused on predictive modeling or forecasting.
A Master's degree in a quantitative field such as Statistics, Mathematics, Data Science, or Computer Science.
Advanced proficiency in Python and SQL for data manipulation and model development.
Demonstrated experience with machine learning frameworks and production-grade data pipelines.
Ability to work effectively in an operational capacity, managing real-time model performance and troubleshooting issues as they arise.
Nice to have
Moderately complex problems are independently solved by pinpointing root causes and proposing scalable, well-reasoned solutions.
A holistic view is taken of how data, models, and technology interact within the forecasting ecosystem.
Rigorous mathematical techniques from econometrics and statistics are applied to analyze economic data and generate precise forecasts.
Appropriate AI models are selected and applied to enhance decision-making and create value.
Complex data findings are translated into actionable insights for both technical and non-technical audiences.
Daily operations are balanced with long-term research and development goals.
Familiarity with programming languages such as Fortran and C# is considered a plus.
Experience with power markets or grid infrastructure is beneficial.
Practical notes
This role is based in Bucharest, Romania, with hybrid work安排 of 2 days in the office. The position reports to an Engineering Manager and operates within a global team spanning multiple offices. Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
Good to know
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.