Senior Data Scientist – AI & Data Warehouse Systems

Role Overview: 

We are looking for a Senior Data Scientist to help build the next generation of analytics on top of our enterprise data warehouse. This role is focused on a practical and increasingly important challenge: how AI interacts with structured business data.

The successful candidate will help design and build systems where AI can understand, query, validate, reconcile, and reason on analytical data in a reliable and controlled way.

This is not a traditional dashboarding role, and it is not a pure research-focused Data Science role. The primary focus is practical implementation of AI systems inside a modern data warehouse environment.

The role combines several disciplines:

•         Data Science
•         AI / Large Language Models (LLMs)
•         Data Warehousing & Analytics Engineering
•         Machine Learning
•         Data Quality & Validation
•          AI Agentic Workflows

What We Are Building

We are building an AI-ready analytical environment on top of our data warehouse where AI can reliably interact with structured business data.

Examples include:

•         Natural-language analytics on warehouse data
•         AI-driven reconciliation and validation
•         Analytical copilots
•         Data quality automation
•         Business metric validation
•         Automated anomaly detection
•         Multi-agent analytical workflows

The focus is not AI for the entire company. The focus is specifically AI working with analytical and financial data inside the data warehouse ecosystem.

Key responsibilities

1. Build AI-Ready Data Warehouse Solutions: Design analytical data structures that can be reliably consumed by both humans and AI systems. Move business logic into the warehouse, improve semantic consistency, define reusable metrics, and ensure analytical correctness across systems.

2. Design AI Interactions with Warehouse Data: Help define how AI interacts with warehouse data through prompts, semantic context, retrieval logic, validation mechanisms, and AI workflows. Ensure AI produces correct and trusted answers.

3. Work with LLMs & AI Agents: Work hands-on with technologies such as ChatGPT, Claude, OpenAI APIs, and AI agents. Test, evaluate, and improve AI outputs. Design workflows where multiple AI agents collaborate to solve analytical problems.

4. Ensure Metric Correctness: Help define how AI interprets business metrics and financial logic. Ensure AI correctly understands the difference between concepts such as revenue, cost, gross revenue, net revenue, trading volume, cash movement, accounting movement, balances, and position changes.

5. Improve Data Governance & Reliability: Ensure AI interacts with data in a controlled and secure manner. Help implement validation mechanisms, prevent sensitive data exposure, and improve trust in AI-generated outputs.

Scope:

Data Platform

• Build clean, reusable analytical data layers in BigQuery
• Move business logic from BI into the warehouse
• Define metrics, dimensions, and semantic consistency

AI Interaction

• Enable reliable AI ↔ data interaction
• Design schemas + instructions so AI produces correct outputs
• Test and refine real AI usage (not theory)

Access & Governance

• Implement data-layer access control (not BI-layer)
• Row/column-level security, role/attribute-based access
• Ensure consistent behavior across BI, AI, and internal tools
• Ensure metric reconciliation across different data sources 

Prevent:

• sensitive data leakage
• shadow metric layer
• uncontrolled query cost

Automation:

• Replace manual data workflows with AI-driven processes
• Build agents for reporting, validation, and internal analytics

What we are looking for:

•         Strong SQL and analytical data modeling
•         Experience working with analytical databases (BigQuery, Snowflake, Databricks, Redshift or similar)
•         Practical experience with LLMs (ChatGPT, Claude, OpenAI APIs or similar)
•         Experience building AI-driven workflows or agent-based systems
•         Strong analytical and problem-solving skills
•         Understanding of machine learning fundamentals

Nice to have: 

•         Experience training or fine-tuning models using historical/labeled data
•         Entity resolution / record matching
•         Classification models and probability scoring
•         RAG / vector databases
•         dbt / semantic layers
•         BigQuery optimization
•         Finance, brokerage, or fintech experience
•         Data governance or fine-grained access control

What we offer:

• Competitive salary that reflects your experience and the value you bring.
• Flexibility that fits your life — work from home, from our office, or a mix of both. You decide what works best.
• Flexible benefits package — choose the options that suit your life, not a one-size-fits-all bundle.
• A genuinely good place to work — an informal, collaborative culture where ideas are heard and bureaucracy stays out of your way.
• Continuous learning — ongoing training, education programs, and the support to deepen your expertise in a fast-moving industry.
• Connection beyond your desk — events that bring our teams together to network and celebrate.
• Global exposure — work side by side with talented colleagues from all over the world, across a business serving clients in 100+ countries.