Cyndx is hiring a Lead Data Scientist to architect and build machine learning models that leverage financial and text data. You will be responsible for designing proprietary AI algorithms, including fine-tuning large language models for semantic search, financial estimations, and trend predictions.

What You'll Do

Design and develop data engineering pipelines to ingest, transform, and integrate new financial data sources
Maintain and enhance our models using time series analysis, machine learning, and deep learning techniques
Implement and fine-tune open-source LLMs for specialized financial text generation, summarization, and classification tasks
Create and maintain features in our FastAPI middleware service, including developing new endpoints and optimizing existing ones
Participate in code reviews, testing, and deployment processes using modern CI/CD practices
Develop and suggest solutions and strategies to business challenges
Work together with engineering and product development teams

What We're Looking For

4-8 years of experience as a Data Scientist with significant experience in Gen AI, vector databases, and traditional machine learning
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related STEM field
Strong foundation in statistics, probability, calculus, and linear algebra, particularly as applied to time-dependent data
Experience with Python programming and data science libraries (NumPy, Pandas, scikit-learn, PyTorch or TensorFlow)
Knowledge of NLP concepts and techniques, including word embeddings, transformer architectures, and large language models
Experience in productionizing code and deploying models to cloud environments, preferably GCP (BigQuery, Cloud Run, GCS) or similar platforms
Proficiency in SQL and experience working with relational databases
A strong research mind to read and understand the latest research in AI and NLP

Nice to Have

Experience with financial data, financial modeling, or quantitative analysis
Familiarity with time series forecasting techniques
Experience with infrastructure-as-code tools like Terraform
Knowledge of Docker containerization and Kubernetes orchestration
Prior work with agentic AI systems or RAG (Retrieval-Augmented Generation) architectures
Understanding of MLOps practices and tools
Experience contributing to open-source projects or building reusable software components

Technical Stack

Languages & Core Libraries: Python, NumPy, Pandas, scikit-learn, PyTorch, TensorFlow, SQL
Frameworks & Services: FastAPI, GCP, BigQuery, Cloud Run, GCS
Infrastructure & Tools: Docker, Kubernetes, Terraform