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Junior Financial Analyst

1 month ago

Tysons Corner, United States PivotalEdge Capital, LLC Full time
Job DescriptionJob Description

Role Overview:
We are seeking a highly curious and analytical Financial Analyst with 2-3 years of experience. This role is crucial to our mission of leveraging data to drive investment decisions. The ideal candidate will have a strong background in corporate financial and operational analysis, coupled with a relentless curiosity that drives them to constantly ask "why?" and dig deeper into data. Reporting directly to the founder, you will have the unique opportunity to impact our investment strategies by uncovering actionable insights from complex datasets.

Key Responsibilities:

Data Acquisition and Preparation
- Data Identification: Determine relevant data sets to answer specific business questions.
- Data Extraction: Acquire data from various sources, including databases, APIs, and spreadsheets using tools like SQL and Python.
- Data Cleaning: Handle missing values, outliers, inconsistencies, and duplicates using statistical methods and programming techniques.
- Data Transformation: Convert raw data into suitable formats for analysis through normalization, aggregation, and feature engineering.
- Data Validation: Ensure data accuracy and consistency with rigorous quality checks.

Exploratory Data Analysis (EDA)
- Univariate Analysis: Explore individual variables using descriptive statistics, histograms, and box plots.
- Bivariate Analysis: Analyze relationships between two variables with scatter plots and correlation matrices.
- Multivariate Analysis: Investigate relationships among multiple variables using techniques like PCA and factor analysis.
- Data Visualization: Create informative charts and graphs to effectively communicate findings using tools like Tableau, Power BI, or Excel.

Statistical Analysis
- Hypothesis Testing: Formulate and test hypotheses using statistical tests like t-tests and ANOVA.
- Regression Analysis: Model relationships between variables using linear, logistic, or multiple regression techniques.
- Time Series Analysis: Analyze time-based data to identify trends and forecast future values.
- Predictive Modeling: Understand model-building techniques to predict future outcomes using decision trees, random forests, and machine learning algorithms.

Communication and Collaboration
- Storytelling: Transform data into compelling narratives that resonate with stakeholders.
- Data Visualization: Create clear and visually appealing dashboards and reports to communicate findings.
- Presentation Skills: Deliver presentations tailored to the audience's technical expertise.
- Collaboration: Work closely with business stakeholders, data engineers, and data scientists to understand requirements and provide valuable insights.

Technical Skills
- Programming Languages: Proficiency in Python, R, or SQL.
- Statistical Knowledge: Solid understanding of statistical concepts, including probability, hypothesis testing, and regression analysis.
- Data Manipulation and Cleaning: Expertise in tools like Pandas (Python) for data cleaning and preparation.
- Data Visualization: Skilled in tools like Tableau, Power BI, or Python libraries (Matplotlib, Seaborn) for creating visuals.
- Database Management: Knowledge of SQL and NoSQL databases for handling large datasets.
- Machine Learning (Optional): Basic understanding of machine learning concepts for advanced analytics and predictive modeling.

Soft Skills
- Problem-Solving and Critical Thinking: Ability to tackle complex challenges with analytical thinking.
- Communication Skills: Strong written and verbal communication skills to translate technical findings into actionable insights.
- Business Acumen: Deep understanding of corporate financial and business analysis.
- Collaboration: Ability to work effectively with teams across the organization.
- Attention to Detail: Meticulous attention to data accuracy and consistency.
- Curiosity: A mindset that drives you to explore data from different angles and constantly ask "why?".