Sr. Advanced Data Scientist leveraging advanced analytics and data science at Honeywell. Developing solutions for business growth and operational efficiency in the Atlanta office.
About the role
- Senior Data Scientist building AI-driven products transforming commerce transactions into investment signals. Collaborating with high-performance teams to ensure compliance and governance standards.
Responsibilities
- Monetize Transaction Intelligence: Design and develop inventive products that leverage AI/ML/DL models to transform data into actionable investment signals and alpha-generating opportunities.
- Architect Financial AI: Build custom GenAI, NLP, and LLM models for high-velocity stream processing, focusing on extracting market sentiment and spending trends from structured transaction data and unstructured metadata.
- Next-Gen Frameworks: Implement LangChain and LlamaIndex to develop RAG and Agentic AI frameworks that enables interaction with complex payment datasets.
- Quantitative Collaboration: Work in a high-performance team environment, collaborating with Product Managers, Payment System Experts and Engineering to deploy and monitor production-grade AI & ML models.
- Strategic Synthesis: Distill complex quantitative insights into high-level investment theses for executive leadership and sophisticated external stakeholders.
- Data Stewardship & Compliance: Partner with the Data Usage Committee, Legal, and Compliance teams to ensure data privacy and adherence to strict data usage rights.
Requirements
- Bachelor’s degree in a highly quantitative field such as Computer Science, Mathematics, Artificial Intelligence, Financial Engineering, or Statistics.
- 5+ years of experience leveraging massive structured (transactional) and unstructured datasets to develop tactical investment insights using ML, RAG, and NLP.
- 5+ years of experience formulating research problems, designing back tests, and implementing production-ready solutions in a financial or high-growth tech environment.
- Proficiency in tokenization and embeddings, with hands-on experience tuning and deploying Large Language Model architectures (e.g., LLaMA, BERT, or Transformers) for financial domain tasks.
- Experience with Python, PyTorch, TensorFlow, and Agentic AI frameworks.
- Deep familiarity with Time Series Econometrics, Quantitative Investment Strategies, and Alternative Data (specifically merchant and banking data).
- Mastery of statistical techniques including regression, classification, clustering, and non-stationary time series analysis.
- Working knowledge of Databricks, Snowflake, or high-performance database systems, and experience with Azure ML, AWS SageMaker or IBM Watson.
Benefits
- Annual incentive opportunity in cash bonus and equity awards
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