Data Scientist at IBM in BR

The Data Analyst Scientist is responsible for collecting, analyzing, and interpreting complex datasets to generate actionable business insights and support data-driven decision-making. The role combines traditional data analysis with advanced statistical and machine learning techniques to identify trends, develop predictive models, create analytical solutions, and communicate findings clearly to business and technical stakeholders.

Key Roles and Responsibilities

* Data Analysis and Exploration: Analyze large and complex datasets to identify trends, patterns, correlations, anomalies, and business opportunities.
* Data Preparation: Extract, clean, transform, integrate, and validate data from multiple sources to ensure accuracy and consistency for analytical activities.
* Statistical Analysis: Apply descriptive and inferential statistical techniques, including hypothesis testing, regression, segmentation, and forecasting.
* Predictive Modeling: Develop and evaluate machine learning and statistical models for classification, regression, forecasting, clustering, and other business use cases.
* Business Intelligence: Build dashboards, reports, and analytical visualizations using tools such as Power BI, Tableau, or similar BI platforms.
* SQL and Data Extraction: Develop complex SQL queries to extract, manipulate, aggregate, and analyze data from relational databases and data warehouses.
* Programming and Analytics: Use Python or R and analytical libraries such as Pandas, NumPy, scikit-learn, and related frameworks for advanced data analysis.
* Data Visualization and Storytelling: Translate complex analytical findings into clear visualizations, recommendations, and business narratives for technical and non-technical audiences.
* Business Requirement Analysis: Partner with stakeholders to understand business challenges, define KPIs and analytical requirements, and translate them into data-driven solutions.
* Model Evaluation: Assess analytical and ML models using appropriate metrics, validate results, and identify opportunities for optimization.
* Data Quality and Governance: Support data-quality controls, documentation, lineage, governance, privacy, and security requirements.
* Automation: Automate recurring analytical activities, reporting, and data-processing workflows to improve efficiency and reduce manual effort.
* Cross-functional Collaboration: Work closely with Data Engineers, Data Scientists, ML Engineers, Business Analysts, Product Owners, and business stakeholders to deliver end-to-end analytical solutions.