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2024 Senior Data Scientist Internship - Corporate Development



Data Science
New York, NY, USA
Posted on Friday, November 3, 2023
As a Data Scientist at IBM, you will help transform our clients’ data into tangible business value by analyzing information, communicating outcomes, and collaborating on product development. Work with Best in Class open source and visual tools, along with the most flexible and scalable deployment options. Whether it’s investigating company insights or acquisition opportunities, you will work to solve real-world problems for the industries transforming how we live.

Your Role and Responsibilities
This position is an internal Staff Data Scientist Intern role with IBM Corporate Development (Mergers & Acquisitions). Office locations for the position are
NYC, NY. The start and end dates for this internship are during the summer of 2024 (3 months)

You will work alongside consultants, deal leaders, data scientists, and developers to enhance business performance through analytics and data science-focused initiatives. You will help transform data into tangible business value by analyzing information, communicating outcomes, and collaborating on product development.

A typical day in the life of this role will include the following:

  • Providing thought leadership and strategic thinking to translate business problems into analytical frameworks
  • Designing, researching, and developing natural language processing (NLP) models and machine learning algorithms to get insights from structured and unstructured data
  • Operating as a subject matter expert on statistical analysis and machine learning for modeling, writing code, testing, and validating
  • Interpreting output and performance of models in business terms, and communicating findings to the team and stakeholders
  • Collaborating with cross-functional teams to understand their business needs; formulating and completing end-to-end analysis

Are you passionate about harnessing the power of big data and analytics to
address real-world business problems and play a key role in influencing change? If so, we want you to apply.

Required Technical and Professional Expertise

  • BS or MS in Data Science, Machine Learning, Statistics, or related STEM field
  • Proficiency in Python
  • Experience and/or coursework with common Python libraries used by data scientists (e.g., NumPy, Pandas, SciPy, scikit-learn, matplotlib, Seaborn, etc.)
  • Experience and/or coursework in statistics and machine learning
  • Experience and/or coursework working with structured and unstructured data
  • Experience and/or coursework in deep learning and natural language processing
  • Understanding of database technologies, data structures and SQL
  • Demonstrated ability to think strategically about business and technical challenges in an enterprise setting
  • Strong presentation and communication skills with the ability to explain complex analytical concepts to people from other fields
  • Demonstrated ability to share data science and machine learning experiment results with both technical and non-technical people
  • Innovative mindset and an ability to take calculated risks
  • Demonstrated a strong desire to learn new skills and excitement towards undertaking challenging transformations

Preferred Technical and Professional Expertise

  • Students pursuing Ph.D. in Machine Learning, Statistics, or related STEM field
  • Understanding of coding requirements for production applications (e.g., modularity, documentation, quality assurance, testing, logging, etc.)
  • Familiarity with Generative AI, LLMs, and recent developments in Artificial Intelligence
  • Knowledge of big data technologies (e.g., spark, Hadoop, etc.)
  • Understanding of code versioning
  • Experience in Agile development
  • Experience deploying analytical models to solve business problems
  • Experience in a project management function
  • Ability to own analytical work from inception to completion, preparing project plans, creating and monitoring performance metrics
  • Ability to manage key stakeholder expectations and asset delivery