Data Science - STUDENT CONVERSION

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. 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. 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. • 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. Uma carreira na IBM Consulting é construída sobre relacionamentos duradouros com clientes e colaboração próxima em todo o mundo. Você trabalhará com empresas líderes em diversos setores, ajudando-as a moldar suas jornadas de nuvem híbrida e IA. Com o suporte de nossos parceiros estratégicos, a robusta tecnologia IBM e a Red Hat, você terá as ferramentas para impulsionar mudanças significativas e acelerar o impacto para os clientes. Na IBM Consulting, a curiosidade impulsiona o sucesso. Você será encorajado a desafiar a norma, explorar novas ideias e criar soluções inovadoras que entregam resultados reais. Nossa cultura de crescimento e empatia foca no seu desenvolvimento de carreira a longo prazo, valorizando suas habilidades e experiências únicas. Como Cientista de Dados: Analytics, você trabalhará com clientes e especialistas do setor para estruturar problemas, analisar dados, aplicar tecnologias de machine learning e desenvolver insights preditivos. Você prestará serviços voltados ao cliente para a solução cognitiva de analytics preditivo de próxima geração da IBM, entregando insights impulsionados pelo Watson Analytics. Suas principais responsabilidades incluirão: • Análise de Dados do Cliente: Você trabalhará com clientes para analisar seus dados, identificar padrões e desenvolver insights preditivos que os ajudem a resolver problemas de negócios, prever o futuro ou tomar medidas. • Aplicação de Machine Learning: Você aplicará tecnologias de machine learning aos dados do cliente para desenvolver modelos preditivos e entregar insights que impulsionem resultados de negócios. • Estruturação de Problemas: Você colaborará com clientes e especialistas do setor para estruturar corretamente os problemas e identificar oportunidades para soluções baseadas em dados. • Desenvolvimento de Insights Preditivos: Você utilizará técnicas e modelos de ciência de dados para desenvolver insights preditivos que ajudem os clientes a tomar decisões de negócios informadas. • Entrega de Soluções ao Cliente: Você prestará serviços voltados ao cliente, entregando insights e soluções que atendam às suas necessidades e expectativas. • Compreensão em Ciência de Dados: Expertise comprovada em uma ou mais áreas da ciência de dados, com ampla experiência em diversas técnicas e modelos de ciência de dados. • Proficiência em Ferramentas Especializadas: Domínio no uso de SPSS ou ferramentas similares em nível avançado, com capacidade de alavancar ferramentas IBM e modelos gerais de ciência de dados. • Aptidão em Aplicação de Machine Learning: Experiência na aplicação de tecnologias de machine learning para desenvolver modelos preditivos e gerar insights que impulsionam resultados de negócio. • Gestão de Análise de Dados e Desenvolvimento de Insights: Habilidade em analisar dados, identificar padrões e desenvolver insights preditivos para embasar decisões de negócio. • Facilidade com Soluções de Analytics Preditivo Cognitivo: Familiaridade com soluções de analytics preditivo cognitivo, como aquelas baseadas em Watson Analytics, e capacidade de entregar serviços voltados ao cliente. • Compreensão em Ciência de Dados Ampla: Experiência com diversas técnicas e modelos de ciência de dados, com capacidade de aplicá-los a diferentes problemas de negócio e desenvolver insights preditivos. • Proficiência em Ferramentas Avançadas: Experiência com ferramentas da IBM e modelos gerais de ciência de dados, com capacidade de alavancá-los para entregar soluções ao cliente. • Aptidão em Análise Preditiva Cognitiva: Experiência com soluções de análise preditiva cognitiva, como as desenvolvidas com Watson Analytics, com capacidade de aplicar esse conhecimento para entregar serviços voltados ao cliente. Brazil Data & Analytics Remote Entry Level NO City, BR NO City (0022) IBM Brasil-Industria, Maquinas e Servicos Limitada A/B Testing Amazon Web Services Analytics Artificial Intelligence Automation Business Intelligence Tools Business Requirements Communication Dashboard Data Analysis Data Governance Data Modeling Data Quality Data Science Data Visualization Data Warehousing Databases Documentation Electronic Design Automation Experimental Design Forecasting Git Machine Learning Metadata Microsoft Azure Microsoft Power BI Predictive Analytics Predictive Modeling Probability Theory Computer Programming Python R Programming Language Structured Query Language Security Data Segmentation Spark Spreadsheets Statistical Modeling Statistics Tableau Testing Validation Detail-oriented Google Cloud Platform Pyspark Analytical thinking Data Cleansing Version Control Exploratory Data Analysis Data Extraction Databricks NumPy Pandas Library Regression Analysis SciPy Scikit-learn Key Performance Indicators Setting Statistical Analysis Azure Synapse Analytics Feature Engineering Matplotlib Seaborn Query Optimization Machine Learning Operations Correlation Analysis Data Storytelling Azure Data Lake Azure Machine Learning Semi-structured Data