Senior Data Scientist

Location: Remote - North America

Department: Data Analytics

Your Role: Alpaca is seeking an experienced Senior Data Scientist to join our remote, distributed team. This role is pivotal in driving insights and innovation from our rich data ecosystem, including financial transactions, customer data, API usage patterns, and third-party sources.

You will be responsible for applying deep predictive and descriptive analytics to unlock strategic value and will be a key player in developing our next generation of agentic AI applications.

What You'll Do

• Lead Advanced Analysis: Design and execute deep predictive and descriptive analyses across our core business domains, including finance, marketing, and product metrics, to identify key trends, optimization opportunities, and strategic insights.
• Develop Agentic AI: Spearhead the research, proof-of-concept (PoC), and full-lifecycle development of data-intensive applications, with a specific focus on building and deploying agentic AI systems.
• Drive Model Lifecycle: Own the end-to-end development of data science solutions, from initial research and data handling (high-volume transactions, metrics) through to deployment, optimization, and maintenance.
• Cross-Functional Collaboration: Work closely with stakeholders in sales, marketing, product, and operations to understand complex business needs and translate them into actionable, data-driven solutions.
• Evolve Capabilities: Contribute to the evolution of Alpaca's data science capabilities by championing best practices in statistical modelling, MLOps, and advanced AI.

Required Qualifications

• Domain Experience: Proven experience as a Senior Data Scientist with direct, hands-on experience in the brokerage, trading, or financial services industry.
• Technical Proficiency: Expert-level proficiency in Python (including common data science libraries like pandas, scikit-learn, etc.) and advanced SQL.
• Data Science Expertise: Deep understanding of statistical methods for supervised and unsupervised learning, with a strong portfolio of building and deploying models in a production environment.
• Cloud & Data Warehousing: Hands-on experience with cloud-native data warehouses (e.g., Snowflake, BigQuery, Redshift; we use Trino & Iceberg) and familiarity with modern ETL processes.
• Full-Stack Mindset: Ability to lead projects through the entire lifecycle (research, development, deployment, maintenance) and a strong understanding of handling high-volume data.
• Collaboration: Excellent communication skills and a proven ability to thrive in a fast-paced, collaborative environment, translating complex findings for non-technical stakeholders.

Preferred Qualifications (Nice-to-Have)

• Agentic AI / LLMs: Direct experience building applications using Large Language Models (LLMs) or agentic AI frameworks (we use Google Agent Engine).

Semantic Layers: Familiarity with data modelling and semantic layers (e.g., dbt Semantic Layer, Cube.js, LookML).