AI Data Scientist

About the Role

Schonfeld Strategic Advisors is seeking an AI Data Scientist to help build the data and analytics foundation powering our growing agentic AI capabilities for investment research.

This is a hands-on role that sits at the intersection of AI, data science and data engineering: you will build the pipelines and datasets that feed our internal AI platform, dig into the data itself to analyze it and engineer features, and help shape how it is used for modeling and research.

We're looking for someone equally comfortable designing robust data solutions and exploring data to uncover insights, and who is passionate about applying these skills to complex data problems alongside our investment teams.

Key Responsibilities

Data Pipeline Development

• Design and build scalable, reliable data pipelines to ingest, transform, and deliver structured and unstructured data.
• Implement real-time and batch data processing workflows to meet varying latency requirements.
• Ensure data quality, consistency, and integrity across all pipelines.

Data Analysis

• Explore, clean, and analyze new and existing datasets to build a deep understanding of their structure, quality, and potential applications.
• Engineer features and curate research-ready datasets for training, fine-tuning, and model evaluation.
• Conduct exploratory analysis to assess data coverage, surface insights, and validate suitability for research and AI use cases.

AI Data Infrastructure

• Build and maintain data infrastructure for AI/ML workloads, including vector databases and semantic search systems.
• Design data schemas and storage solutions that support efficient retrieval and processing for LLM applications.
• Contribute to data versioning, lineage tracking, and observability for AI training and inference pipelines.

Integration & Collaboration

• Partner with cross-functional teams, including AI engineers, researchers, and business stakeholders, to understand data needs and design solutions.
• Collaborate with infrastructure teams on cloud architecture, security, and compliance requirements.

Required Qualifications

Technical Skills

• Programming: Strong proficiency in Python and SQL (experience with an additional language such as Java, Scala, or Go is a plus)
• Pipeline Development: Building and orchestrating production data pipelines and ETL/ELT workflows (e.g., Apache Airflow, Prefect, Dagster)
• Data Modeling & Storage: Designing schemas and working with SQL and NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB, Elasticsearch)
• Data Analysis & Modeling: Analyzing data and developing models using statistical and machine learning tools such as XGBoost, PyTorch, and TensorFlow; performing feature engineering, model training, and evaluation.
• Cloud Platforms: Working with a major cloud provider (AWS, GCP, or Azure) and core storage and compute services
• AI/ML Data: Preparing and serving data for ML/AI systems, including vector databases (e.g., Pinecone, Weaviate, Qdrant) and embedding pipelines

Preferred Experience

• Financial domain knowledge - e.g., quantitative research or data science experience at a buy-side or sell-side firm
• Experience supporting LLM applications or RAG (Retrieval Augmented Generation) systems
• Familiarity with distributed computing frameworks (Spark, Flink) or large-scale data platforms
• Familiarity with financial data sources (market data, fundamental data, alternative data)
• Knowledge of data streaming technologies (Kafka, Kinesis, Pub/Sub)
• Experience with data quality frameworks (Great Expectations, Deequ)

Professional Skills

• Bachelor's, Master's, or PhD in Computer Science, Data Science, or a related field
• Strong problem-solving skills and attention to detail
• Excellent communication skills with ability to translate technical concepts for non-technical stakeholders
• Experience working in fast-paced, collaborative environments
• Self-motivated with ability to manage multiple priorities