IBM is a leader in Infrastructure & Technology, delivering resilient solutions that power innovation. As a Data Scientist, you will convert business problems into analytics solutions, assist in developing and delivering analytic solutions, and communicate effectively with senior team members to drive business outcomes.
Learning how to Translate Business Problems into Analytic Problems: Jointly analyze business issues, understand senior team members’ thinking, and start making the connection to data sources, data types, data science modeling objectives, and appropriate models
Develop Analytics Solutions: Assist in the development and delivery of analytic solutions that realize business value. Tasks include examining data from multiple disparate sources, gaining a fuller understanding of the how the use-case, the data, and the models combine to uncover hidden insights for competitive advantage, and then taking this informed approach to jointly develop the data science modeling solution under supervision
Build Data Sets and Models: Assist in curating data and developing and applying data science models—which may include deterministic, descriptive, diagnostic, predictive, and prescriptive models per the use-case—whose outputs will be consumed by analysts and decision-makers. Also, start developing your own intuition to determine what your team’s delivered solution means and how you believe it can drive business outcomes
Communicate with senior team members: Effectively communicate with senior team members your work progress, support/explain the work you have done, explain blockers or anomalies, demonstrate initiative, ownership mentality, and independent thinking as appropriate
Qualification
Required
Education, training and experience in statistics, data transformation, data science modeling—or alternatively possess appropriate education and experience in operations research, computer science, or mathematics to analyze business issues and develop analytic solutions
Data-driven problem solving using empirical analyses and data science methods
Experience working with data from multiple disparate sources to extract, transform, derive, dimensionalize, and combine into curated feature data sets that can consumed by data science models
Assisting in developing data science models
Communicating findings and recommendations to business leaders - having observed senior data science team members, successfully communicate findings and recommendations designed to influence an organization's approach to solving its business challenges
Hands on programming language experience: Python (minimum: NumPy, Pandas, Sci-Kit Learn), SQL (DB2 or PostgreSQL), SPSS Modeler (optional)
Business software such as Microsoft Office, Slack, Box, and Monday
Preferred
Master's Degree
Structured data modeling algorithms: Supervised learning such as Random Forest, CHAID, C5, XGBoost, Multiple Regression, Logistic Regression, Cox Regression, Poisson Regression, KNN, GLM; Unsupervised learning such as K-Means, PCA, Factor analysis, Anomaly detection; Associative models such as sequence or a priori; Time series models such as ARIMA, Exponential Smoothing, or Prophet; Prescriptive modeling techniques such as integer programming or linear programming
AI Assistants. Utilize LLMs such as IBM Granite, Google Gemini, Microsoft CoPilot, or Anthropic Claude to generate, debug, and optimize code to increase coding efficiency and reduce development time
Possess the ability to learn and use proprietary data and collaboration platforms such as GitHub, IBM Cloud Pack 4 Data (CP4D), Watson Studio, WatsonX
Benefit
IBM is an IT technology and consulting firm providing computer hardware, software, infrastructure, and hosting services.
