Senior Data Scientist

Company & Culture
EXANTE is a pioneering wealth tech company that delivers cutting-edge centralized trading solutions and robust B2B financial infrastructure, driving value through innovative technology.
Our bespoke, internally engineered trading platform provides seamless access to a diverse range of financial instruments, including stocks, ETFs, bonds, futures, and options, all within a single, multi-currency account.
As a rapidly expanding global firm with over 700 talented employees from 70+ nationalities across 70 locations, we are a frontrunner in the financial sector.
Our investment priorities are clear: We prioritize investing in our most valuable asset—our people.
Join us in shaping the future of finance.
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.
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.
Core 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
Experience:
- 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
- Classificatio