Job Title : Data Scientists
Location : Hermitage, TN
Job Duties :
Data Acquisition, Cleaning & Preprocessing
- Assist in collecting, validating, and preprocessing structured and unstructured datasets from internal and third-party financial systems.
- Perform data quality checks, resolve anomalies, and maintain metadata using SQL, Python (Pandas), and Excel.
Exploratory Data Analysis (EDA)
- Conduct exploratory data analysis to identify trends, outliers, and correlations within financial and operational datasets.
- Support the preparation of data summaries, distribution checks, and hypothesis validations.
Automation & Data Pipeline Support
- Assist in developing automation scripts and data pipelines using Python, Excel macros, and RPA tools (e.g., Blue Prism) to streamline data ingestion and transformation.
- Support version control and CI/CD practices using Git repositories.
Predictive Modeling & Forecasting
- Support senior data scientists in building and validating statistical and machine learning models to forecast revenue trends, customer churn, or financial health.
- Participate in refining time-series models and basic regressions using Python (Scikit-learn, StatsModels).
Financial & Business Analysis
- Contribute to financial modeling by evaluating key metrics (e.g., EBITDA, revenue growth, margins) and integrating external macroeconomic indicators into models.
- Work alongside business analysts to align technical models with stakeholder requirements.
Data Visualization & Dashboarding
- Develop and maintain interactive dashboards using Tableau, Power BI, or Python (Matplotlib, Seaborn) to communicate insights to internal stakeholders.
- Automate reporting templates and visualization tools for monthly and quarterly updates.
Documentation & Compliance
- Maintain comprehensive documentation for model assumptions, workflows, data dictionaries, and QA protocols.
- Ensure data practices align with internal governance policies and industry regulations (e.g., GDPR, SOX).
Collaboration & Communication
- Work closely with cross-functional teams including finance, data engineering, and business strategy teams to align analytical efforts with organizational goals.
- Participate in sprint meetings and contribute to shared knowledge repositories.
Model Monitoring & Feedback Loops
- Assist in tracking model performance and accuracy post-deployment using standard KPIs (e.g., RMSE, MAE).
- Help integrate user feedback and error analysis into model retraining cycles.
Professional Development
- Attend internal workshops and training sessions on data science tools and methodologies.
- Stay informed of advancements in machine learning, financial modeling, and analytics platforms.
