Skills

Data Science & Analytics

• Exploratory data analysis (EDA), data cleaning, and feature engineering
• Statistical analysis, hypothesis testing, A/B testing, and KPI tracking
• Supervised and unsupervised machine learning (regression, classification, clustering)
• Model selection, cross-validation, and hyperparameter tuning
• Natural language processing (NLP) with transformer-based models
• Content-based recommender systems and similarity metrics

Programming

• Python (pandas, NumPy, scikit-learn, Matplotlib, Seaborn, Plotly)
• SQL (complex queries, joins, aggregation, query optimisation)
• Git and GitHub (version control, branching, collaborative workflows)

BI & Visualisation

• Power BI — interactive dashboards, DAX, M Query, data modelling
• Excel — PivotTables, Power Query, XLOOKUP, INDEX-MATCH, VBA macros
• Dashboard design for senior stakeholders and non-technical audiences

Databases & Data Handling

• Relational database design and structured data modelling
• Data validation, reconciliation across systems, anomaly detection
• ETL workflows and reporting pipelines, end-to-end data lineage

Web & Deployment

• Flask — building APIs and webhook services for live applications
• Streamlit — interactive data apps for model-serving and visualisation
• API integration with external services (e.g. real-time data fetching)
• Deployment on cloud platforms (Render, Heroku) for live access

Tools & Environments

• Jupyter Notebook, VS Code, PyCharm
• Operational tools: Salesforce CRM and Genesys Cloud (from prior support work)

Strengths

• Clear communication of insights to technical and non-technical audiences
• Commercial mindset — every analytical question tied to a real business decision
• Data quality discipline — treats clean, well-documented data as foundational
• Comfortable in fast-paced, cross-functional environments