Introduction

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You'll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you'll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You'll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Your role and responsibilities

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.

Required technical and professional expertise

- Strong experience in data analysis, data science, statistical analysis, and interpretation of complex datasets.

- Advanced proficiency in SQL, including data extraction, joins, aggregations, transformations, and query optimization.

- Strong programming skills in Python and/or R for data manipulation, statistical analysis, automation, and predictive modeling.

- Hands-on experience with analytical Python libraries such as Pandas, NumPy, SciPy, scikit-learn, Matplotlib, and Seaborn.

- Experience developing machine learning and statistical models, including regression, classification, clustering, forecasting, and segmentation techniques.

- Strong knowledge of descriptive and inferential statistics, hypothesis testing, correlation analysis, probability, regression analysis, and statistical modeling.

- Experience with data visualization and Business Intelligence tools, preferably Power BI, Tableau, or similar platforms, including dashboard and KPI development.

- Experience in data cleansing, transformation, validation, feature engineering, and exploratory data analysis (EDA).

- Ability to work with structured and semi-structured data from databases, data warehouses, APIs, spreadsheets, and other enterprise data sources.

- Understanding of data modeling, data quality, data governance, metadata, privacy, and security principles.





- Experience evaluating analytical and machine learning models using appropriate metrics such as precision, recall, F1-score, ROC-AUC, MAE, RMSE, and R², depending on the use case.

- Knowledge of Git/version control and good practices for maintaining reproducible analytical code and documentation.

- Familiarity with cloud data and analytics environments such as Microsoft Azure, AWS, or Google Cloud Platform.

- Ability to translate business requirements into analytical approaches, KPIs, dashboards, models, and actionable insights.

- Strong data storytelling and communication skills, with the ability to explain complex analytical findings to both technical and non-technical stakeholders.

- Strong analytical thinking, problem-solving skills, attention to detail, and ability to work effectively with Data Engineers, Data Scientists, Business Analysts, Product Owners, and business stakeholders.

Preferred / Additional Expertise

- Experience with Databricks, Spark, or PySpark for large-scale analytics.

- Knowledge of Azure Data Lake, Azure Synapse Analytics, Azure Machine Learning, or equivalent cloud services.

- Experience with time-series analysis and forecasting.

- Familiarity with Generative AI and LLM-based analytics.

- Understanding of A/B testing and experimental design.

- Knowledge of MLOps and the machine learning lifecycle is an advantage, especially when models need to move beyond analysis into production.

Preferred technical and professional experience

- Broad Data Science Exposure: Exposure to various data science techniques and models, with the ability to apply them to different business problems and develop predictive insights.
- Advanced Tool Usage: Experience working with IBM tools and general data science models, with the ability to leverage them to deliver client solutions.
- Cognitive Predictive Analytics Knowledge: Exposure to cognitive predictive analytics solutions, such as those powered by Watson Analytics, with the ability to apply this knowledge to deliver client-facing services.

IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

#J-18808-Ljbffr

📌 Data Science - STUDENT CONVERSION (Bra)
🏢 International Business Machines
📍 Bra