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.