Data Scientist & Analytics Strategist - Hybrid Cloud AI

IBM Consulting is seeking a Data Analyst Scientist to collect, analyze, and interpret complex datasets, generating actionable insights and supporting data-driven decisions. You will apply advanced statistics and ML techniques to identify trends, build predictive models, and create analytics solutions for stakeholders across the business and technical domains.

You will work with Data Engineers, Data Scientists, and Analysts to develop dashboards, reports, and KPI visualizations using Power BI,

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. 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 BI 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 metrics such as precision, recall, F1-score, ROC-AUC, MAE, RMSE, and R². 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.