About
At IBM Infrastructure & Technology, we design and operate the systems that keep the world running. From high-resiliency mainframes and hybrid cloud platforms to networking, automation, and site reliability. Our teams ensure the performance, security, and scalability that clients and industries depend on every day. Working in Infrastructure & Technology means tackling complex challenges with curiosity and collaboration. You'll work with diverse technologies and colleagues worldwide to deliver resilient, future-ready solutions that power innovation. With continuous learning, career growth, and a supportive culture, IBM provides the opportunities to build expertise and shape the infrastructure that drives progress.
Your role and responsibilities
As a Data Scientist you will assist in converting business problems into analytics solutions using your education, training, and experience in empirical and data science skills, along with the process knowledge you will acquire as a member of the data science team. You will assimilate the client business issues and the analytic translations formulated by senior team members, perform data qualification to determine their fitness for purpose, assist in formulating hypotheses for the use case, perform analyses as directed and test conclusions that either confirm or disprove the hypotheses. Then you will work with others to develop the solution. Your primary responsibilities will include:
• 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.
Required technical and professional expertise
• Statistics and Modeling Foundation: 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 Analysis, Interpretation and Modeling: 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.
• Business Acumen and Communication: 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). Also: business software such as Microsoft Office, Slack, Box, and Monday.
Preferred technical and professional experience
• 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. Must possess enough knowledge on what the coding solution should be (skill in using or optimizing prompts is not enough). Also, possess the ability to learn and use proprietary data and collaboration platforms such as GitHub, IBM Cloud Pack 4 Data (CP4D), Watson Studio, WatsonX.
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.
Languages
- English
Notice for Users
This job comes from a TieTalent partner platform. Click "Apply Now" to submit your application directly on their site.