IBM is looking to solve business and technical problems for its clients across the globe by advancing the hybrid cloud and AI journey
Requirements
- Familiarity with one or more scripting languages (Python preferred), or a proven computer science foundation.
- Demonstrate familiarity or interest in statistical analysis or data mining through previous internships, personal/academic projects, hackathons, and/or publications.
- General familiarity with databases, data-engineering tools (SQL, spark) and cloud platforms (e.g., IBM Cloud, Azure, AWS). Experience with NLP/LLM/GenAI is a plus.
- Experience using machine-learning/data science libraries in python (scikit-learn, SciPy, pandas, PyTorch) is a plus.
Responsibilities
- Mentored Analytical Support: Receive mentorship from diverse professionals in science engineering and consulting applying analytical rigor and statistical methods to predict behaviors.
- Data Integrations: Develop skills in writing efficient and reusable programs to cleanse integrate and model data. Evaluate model results contributing to data-driven insights.
- Effective Communication: Assist in conveying analytical results to both technical and non-technical audiences, refining your ability to communicate complex findings.
- Tech-Driven Data Transformer: Utilize program languages like Python to build data pipelines, extracting and transforming data from repositories to consumers. Gain exposure to cloud platforms, ETL tools, and data integration, expanding your tech toolkit.
Other
- Currently pursuing a quantitative degree in Computer Science, Statistics, Mathematics, Engineering, or a related field.
- High school diploma and active pursuit of a higher education degree in a relevant field.
- IBM will not provide visa sponsorship for this position. Eligible candidates must not require sponsorship now or in the future to be considered for this role.
- Average on-site work requirement at the local IBM office is at least 3 days per week.
- Candidates should be willing to travel up to 100% if needed, based on work assignments.
