Job Description
Quantitative Data Analyst
We are looking for a precise, curious, and capable Quantitative Data Analyst to research and analyze large-scale financial market data — from order- and trade-level records to time series. The ideal candidate enjoys working with large raw datasets, asks the right questions, and delivers reliable, reproducible analysis.
Responsibilities
Prepare, clean, and validate large-scale market datasets
Analyze time series as well as order-level and trade-level data
Design, implement, and evaluate indicators, hypotheses, and statistical models
Build fast, reproducible, and reliable analytical workflows
Clearly document methodology, assumptions, and results
Requirements
Strong proficiency in Python and its data analysis stack: Polars, Pandas, NumPy, and Jupyter
Hands-on experience processing large datasets with Polars, especially the Lazy API
Solid background in statistics, probability, and time-series analysis
Ability to detect errors, missing data, timestamp inconsistencies, and other data anomalies
Familiarity with SQL, Linux, and Git
High attention to detail, an analytical mindset, and the ability to carry out independent research
Ability to read technical documentation and papers in English
Preferred Qualifications
Experience analyzing financial or high-frequency (tick) data
Familiarity with order books, market microstructure, and backtesting
Experience optimizing processing speed and memory usage
Familiarity with machine learning on time-series data
Full professional fluency in English is a strong plus
Educational Requirements
M.Sc. or Ph.D. in Mathematics, Statistics, Physics, Computer Science, Data Science, Artificial Intelligence, or a related field, from a reputable public university in Tehran
Current M.Sc. and Ph.D. students at Sharif University of Technology in Mathematics, Physics, Data Science, or Artificial Intelligence are also encouraged to apply
Gender: Male candidates only
