Quantitative Researcher
$180000 USD
Onsite WORKING
Location: United States (Massachusetts) Type: Permanent
Exciting Opportunity to join a start up fund and work extremely closely with the founder.
Responsibilities:
● Research Support: Assist the founder in the research lifecycle: validating ideas, performing statistical analysis on market data, conducting literature reviews, refining existing models, and running backtests.
● Data Wrangling & Quality Assurance: Take ownership of more complex data cleaning, validation, normalization, and quality control tasks. This is particularly critical for challenging datasets like minute-level global data and especially non-standardized crypto data, ensuring data integrity for both research and trading. Collaborate with the QD to enhance data pipelines and storage.
● Feature Engineering: Explore, develop, and test novel predictive features derived from raw or cleaned data provided by FactSet or other sources. This involves transforming data into forms more suitable for modeling.
● Data Exploration & Signal Analysis: Utilize statistical methods, data visualization, and potentially machine learning techniques to explore datasets, identify patterns, and uncover potential alpha signals.
● Maintaining Research Environment: Ensure that data, tools (like Jupyter notebooks), and libraries needed for quantitative research are organized, accessible, and functional for the team.
Skills:
● Quantitative Background: Strong foundation in mathematics, statistics, econometrics, or a related field.
● Analytical Programming: Proficiency in programming languages commonly used for data analysis and statistical modeling, such as Python (with libraries like Pandas, NumPy, SciPy, Scikit-learn) or R.
● Data Manipulation: Expertise in handling, cleaning, transforming, and merging large and potentially messy datasets.
● Financial Data Understanding: Familiarity with the structure and nuances of
financial market data across different asset classes.
● Statistical Modeling / ML Fundamentals: Understanding of regression analysis, time series analysis, hypothesis testing, and basic machine learning concepts is highly valuable.
● Attention to Detail: Meticulous approach to data handling and analysis to ensure accuracy
Reference: QR/ NW/AHU/002
#alhu
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