Data Analyst/Wrangler - Trading Insights - Chicago

[Recruiter-Jim]

Reference Data Analyst/Wrangler - Trading Insights - Onsite in Chicago.​

Comp Range: $250-$300k​

About the Role:

Do you want to work for a top tier trading firm?
Do you thrive in a fast-paced environment where new and intriguing datasets are constantly arriving?
Are you energized by the challenge of being the first to explore these datasets and uncover hidden gems of information?
If so, then this is the opportunity for you!

We are seeking a highly motivated and inquisitive Data Analyst/Wrangler with a passion for conducting initial explorations and analyses of diverse datasets.
You will be the "first pass" on new data, responsible for identifying potentially valuable information and communicating your findings to specialized data science teams.

Imagine this: a new dataset lands on your desk. You dive in, explore its nuances, and through your keen analytical skills, uncover a fascinating trend or anomaly. You then present your findings to the relevant data science team, essentially saying, "Come over hear and look at this. This is interesting for these reasons...here are some initial insights...now go take it and run with it!" That's the kind of impact you'll have in this role.

You will be working with a variety of data, including:

• Traditional: Financial market data (e.g., stock prices, interest rates), economic indicators (e.g., GDP, inflation), company fundamentals (e.g., earnings reports, balance sheets).
• Non-Traditional: Social media sentiment, news articles, weather patterns, satellite imagery, and other alternative data sources that could provide a unique edge in the markets.

This role requires a strong understanding of financial markets and the ability to connect seemingly disparate data points to uncover hidden opportunities. You'll need to be comfortable working with structured and unstructured data, and have a natural curiosity for discovering valuable signals within noisy datasets.

What we need:

We need someone with strong analytical skills who can:

• Rapidly assess and explore new datasets.
• Conduct insightful exploratory analysis of reference data.
• Clearly communicate their findings to data scientists.
• Prepare data for further analysis by specialized data science teams.

What we don't need:

This role is not for aspiring data scientists. We are not looking for someone to:

• Build complex machine learning models.
• Develop novel algorithms for predictive modeling.
• Focus primarily on statistical analysis and hypothesis testing.

The Difference:

• Data Analysts focus on understanding and interpreting existing data to extract meaningful insights and inform business decisions. They are skilled in data visualization, exploratory analysis, and communicating findings effectively.
• Data Scientists go beyond analysis to build predictive models, develop algorithms, and conduct advanced statistical analysis. They often have a deeper background in mathematics, statistics, and machine learning.

Responsibilities:

• Data Exploration & Insight Generation:
  • Access and explore diverse datasets, including traditional and non-traditional reference data, from various sources.
  • Conduct initial data quality assessments and cleaning using Pandas, NumPy.
  • Perform exploratory data analysis to identify potential trading signals or opportunities.
  • Critically evaluate the data to determine what is interesting or potentially valuable, and articulate the reasons behind your assessment.
• Analysis & Communication:
  • Develop visualizations and reports to effectively communicate findings using Matplotlib / Seaborn / Ploty.
  • Present insights to data science teams and stakeholders, clearly explaining the potential value and implications of your discoveries.
  • Collaborate with data scientists to define next steps for deeper analysis.
• Data Wrangling:
  • Prepare datasets for handover to data science teams.
  • Ensure data is properly documented and formatted for further analysis.
  • Contribute to the development of data pipelines and workflows.

Qualifications:

Education:

• Bachelor's degree in a quantitative field (e.g., Econometrics, Statistics, Statistical Data Analysis).

Technical Skills:

• Data Collection & Cleaning: Proficiency in gathering data from various sources and cleaning it for analysis (handling missing values, outliers, etc.)
• Programming: Strong programming skills in Python (with Pandas) or R.
• Database Querying: Knowledge of SQL to extract and manipulate data from databases.
• Statistical Fundamentals: Understanding of basic statistical concepts (e.g., descriptive statistics, hypothesis testing, regressions) to draw meaningful conclusions.
• Regression Analysis:
  • Understanding Relationships: Ability to use regression analysis to understand the relationship between variables.
  • Interpreting Results: Knowledge of how to interpret regression coefficients, R-squared values, and p-values to understand the significance of findings.
• Clustering:
  • Identifying Patterns: Familiarity with k-means clustering and its application in identifying patterns and groupings in data.
  • Segmentation and Grouping: Understanding of the broader concept of clustering and its use in segmenting data.
  • Data Exploration: Ability to use clustering as an exploratory tool to understand data structure.
• Dimensionality Reduction:
  • Feature Extraction: Basic understanding of PCA and its potential use in creating new features from existing ones.

Soft Skills:
Communication & Reporting: Excellent ability to clearly communicate findings through reports, presentations, and dashboards, specifically tailored for data scientists.