Apply for the 2026 Data Scientist Co-op at IBM Consulting in University Park, Pennsylvania. Gain high-impact experience building data pipelines, writing Python models, and deploying generative AI agents within the global Financial Services practice.

About the Company: IBM Consulting

IBM Consulting is the global professional services, strategy, and technology transformation division of IBM, recognised as an industry leader in shaping enterprise hybrid cloud architectures and artificial intelligence deployments. IBM Consulting partners with the world’s most innovative financial institutions, insurance conglomerates, and multi-national corporations, delivering high-stakes business strategy, secure cloud operations, and machine learning ecosystem modernisation.

Operating near the academic hub of University Park, Pennsylvania, the Advanced Analytics practice embeds deep technical expertise directly into client environments. Powered by strategic alliances with Red Hat and major public cloud providers, IBM fosters an organisational culture rooted in empathetic leadership, rigorous continuous learning, and responsible technology optimisation to accelerate commercial value.

About the Role: Data Scientist Co-op (Financial Services)

IBM Consulting is seeking an analytically driven, code-proficient Data Scientist Co-op to join its Advanced Analytics division supporting the Financial Services sector. This position is a structured, immersive work-integrated learning opportunity designed for students who want to bridge the gap between heavy academic computing and production-grade enterprise client solutions.

At IBM, co-op students are fully integrated members of agile engineering pods. You will step into a fast-paced environment where you will write efficient, reusable scripts to clean and transform complex institutional financial data. Under the direct mentorship of senior data scientists and consultants, you will build predictive models, design robust data pipelines, and experiment with cutting-edge AI technologies. Top performers during this cohort are fast-tracked into IBM’s elite full-time Associate Program upon graduation.

Key Responsibilities & Data Science Sprints

• Pipeline & ETL Engineering: Utilise Python to architect, build, and maintain scalable data pipelines, extracting and transforming raw financial data from enterprise repositories for consumer applications.

• Predictive Modelling & Integration: Write clean, reusable code to cleanse, integrate, and model multi-source datasets, applying statistical rigor to predict complex market and consumer behaviours.

• Model Evaluation & Optimisation: Evaluate model outcomes, perform validation checks, and translate statistical anomalies into actionable data-driven business insights.

• AI Agent & GenAI Research: Support the development, testing, and deployment of generative AI agents, utilising evaluation-driven development techniques and modern coding automation tools.

• Cross-Functional Technical Communication: Translate complex machine learning configurations and mathematical findings into clear, accessible insights for both technical teams and non-technical stakeholders.

Candidate Prerequisites & Technical Skill Stack

IBM values an insatiable hunger for technical knowledge, strong leadership instincts, and a growth mindset that adapts easily within collaborative, fast-moving agile frameworks.

Educational Background & Baseline Criteria:

• Academic Matrix: Currently pursuing a quantitative degree (Bachelor’s, Master’s, or PhD track) in Computer Science, Statistics, Mathematics, Engineering, Data Science, AI, ML, Cognitive Science, or a matching technical discipline.

• Minimum Baseline Requirement: Possession of a High School Diploma or GED.

Required Core Skills:

• Programming Foundation: Solid computer science fundamentals with hands-on proficiency in at least one scripting language (Python is highly preferred).

• Agile Collaboration: Strong interpersonal skills to navigate dynamic workloads, build relationships, and actively listen to diverse viewpoints in team environments.

• Ownership & Execution: A proactive attitude with a readiness to take full ownership of technical tasks and engineering challenges.

Preferred Technical Experience (The Plus Stack):

• Data Science Libraries: Comfort implementing standard data science and machine learning packages in Python (such as pandas, scikit-learn, SciPy, PyTorch).

• Modern AI Stack: Hands-on exposure or strong interest in Natural Language Processing (NLP), Large Language Models (LLMs), Generative AI, or ML/AI Ops observability platforms.

• Agentic Deployment Tools: Conceptual or practical experience with eval-driven development of AI agents, using coding assistants (e.g., Cursor, Claude Code, Codex).

• Data Infrastructure Tools: General familiarity with relational databases, big data tools (SQL, Apache Spark, Snowflake), and major cloud ecosystems (IBM Cloud, AWS, Azure).

Core Position Specifications

• Position Title: Data Scientist Co-op 2026 – Advanced Analytics

• Industry Focus: Financial Services Sector

• Hiring Enterprise: IBM Corporation (IBM Consulting Business Unit)

• Primary Work Location: University Park, Pennsylvania, United States (Onsite/Hybrid Environment)

• Employment Classification: Supplemental Track (Eligible for up to 8 paid holidays, minimum 56 hours of paid sick time, and the IBM Employee Stock Purchase Plan)

• Core Systems Domain: ETL Pipeline Engineering, Statistical Analysis, Generative AI Agentic Frameworks, Financial Predictive Analytics