Data Scientist-Advanced Analytics

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.

In this role, you'll work in one of our IBM Consulting FutureNow Centers, where we deliver deep technical and industry expertise to a wide range of public and private sector clients around the world. Our delivery centers offer our clients locally based skills and technical expertise to drive innovation and adoption of new technology.

Your role and responsibilities

As a Data Scientist with Advanced Analytics skills, you will leverage deep data and analytics expertise with strong business acumen to address business challenges. You will utilize data preparation, analysis, and predictive modeling to forecast trends and suggest optimizations for improved business outcomes. Your primary responsibilities will include: * Develop Predictive Models: Design and implement predictive models using mathematical optimization, discrete-event simulation, and rules programming to drive business optimization and forecast trends. * Analyze Diverse Data: Manage and analyze diverse data types and structures using tools such as Pandas, NumPy, and Dask, and data visualization with Matplotlib, Seaborn, and Plotly. * Optimize Business Outcomes: Utilize machine learning, statistical modeling, and custom models to optimize business outcomes in applications like supply chain management, pricing, risk assessment, and fraud detection. * Collaborate with Stakeholders: Work with stakeholders to understand business challenges and develop data-driven solutions to address them. * Refine Methodologies: Continuously refine methodologies and approaches to ensure data-driven decision-making and business optimization.

Required education
Bachelor's Degree

Preferred education
Master's Degree

Required technical and professional expertise

* Deep Data Analysis Expertise: Proven ability to leverage deep data and analytics expertise with strong business acumen to address complex business challenges, utilizing data preparation, analysis, and predictive modeling to forecast trends and suggest optimizations for improved business outcomes. * Advanced Programming Skills: Proficiency in programming languages, particularly Python, with experience using development environments like PyCharm, VS Code, and Jupyter Notebooks, and expertise in data manipulation using tools such as Pandas, NumPy, and Dask. * Data Visualization and Machine Learning: Experience with data visualization tools like Matplotlib, Seaborn, and Plotly, and knowledge of machine learning, statistical modeling, and custom models in applications like supply chain management, pricing, risk assessment, and fraud detection. * Database Management and Scripting: Familiarity with managing databases like SQL, MongoDB, Cassandra, PostgreSQL, and MySQL, and scripting abilities in shell scripting, along with experience with version control systems such as Git, GitHub, and GitLab. * Optimization and Statistical Analysis: Experience with optimization tools like IBM CPLEX and Gurobi, and statistical analysis capabilities demonstrated with SPSS, SAS, R, and Python.

Preferred technical and professional experience

* Advanced Data Modeling: Experience with mathematical optimization, discrete-event simulation, and rules programming to drive business optimization and forecast trends. Proficiency in developing predictive models using these techniques is beneficial. Knowledge of optimization tools like IBM CPLEX and Gurobi is also desirable. * Specialized Data Analysis: Familiarity with statistical analysis capabilities demonstrated with SPSS, SAS, R, and Python is advantageous. Experience with data manipulation using tools such as Pandas, NumPy, and Dask is also preferred. * Custom Model Development: Knowledge of machine learning, statistical modeling, and custom models in applications like supply chain management, pricing, risk assessment, and fraud detection is beneficial. Experience with data visualization tools like Matplotlib, Seaborn, and Plotly is also desirable.

Years of Experience:
18-20