Data Scientist

Data ScientistLocation: Hermitage, TNWage: $60,000Job Duties:Data Acquisition, Cleaning & PreprocessingAssist 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 SupportAssist 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 & ForecastingSupport 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 AnalysisContribute 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 & DashboardingDevelop 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 & ComplianceMaintain 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 & CommunicationWork 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 LoopsAssist 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 DevelopmentAttend internal workshops and training sessions on data science tools and methodologies.Stay informed of advancements in machine learning, financial modeling, and analytics platforms.