Bose is looking for students to join their Co-Op Program to apply data science knowledge to real-world challenges, focusing on creating predictive models for analyzing structured and unstructured data, with an emphasis on NLP and GenAI, to fuel innovation and create personalized customer experiences.
Requirements
- Two years of experience with SQL, Python, pandas, and scikit-learn.
- Experience fitting and evaluating classical ML models (Logistic Regression, Random Forests, Multi-layer Perceptron, etc.).
- Prior NLP experience, including working with LLMs, fine-tuning transformer architectures (e.g., BERT, GPT, T5), semantic search, or text summarization.
- Knowledge of deep learning basics.
- Experience with Databricks and Snowflake.
- Familiarity with LangChain, Hugging Face Transformers, Llama 3.x, OpenAI APIs, Pydantic, MLflow, and Streamlit.
- Working knowledge of Git and GitHub.
Responsibilities
- Creating predictive models to analyze structured and unstructured data.
- Apply knowledge of statistics, data science, and ML and your willingness to write functional code to explore problems and uncover patterns in data.
- Engage with business partners and stakeholders to understand their challenges and opportunities.
- Explore large datasets using modeling, analysis, and visualization techniques. Transform the results into insights and recommendations.
- Contribute to the end-to-end development of predictive and prescriptive models for marketing, sales, finance, supply chain, and other business applications.
- Design, build, and optimize workflows to streamline and automate the processing and delivery of data.
- Develop and maintain applications that deliver unique, insightful capabilities to empower end users through interactive and intuitive interfaces.
Other
- Currently enrolled in a bachelor's or master's degree program in a quantitative field such as Computer Science, Statistics, Applied Mathematics, Engineering, Information Science, etc.
- Good interpersonal, communication, and presentation skills.
- Developing expertise in analytics, BI, statistics, data science, or a related area.
- Proven data science experience via an internship, work experience, competitions, etc.
- Timeframe - January-June 2026
