Network Analyst (Data Analyst)

What the job entails:

• You’ll be part of a small, sharp team applying Network Science to real-world business problems inside a high-impact AI exploration unit. We’re turning raw data into meaningful graphs, analyzing connections, detecting hidden patterns, and building tools to help autonomous agents make better decisions.
• You won’t be optimizing dashboards or stuck tweaking marketing reports. You’ll be building intelligence infrastructure.

Your main tasks:

• Transform tabular data into graphs that expose structure and behavior.
• Research, prototype, and deploy graph algorithms to detect communities, centrality, anomalies, and more.
• Build and maintain ETL pipelines for graph data extraction and enrichment.
• Design graph visualizations to support human and machine understanding.
• Collaborate with Data Scientists and LLM Engineers to integrate network-based reasoning into our autonomous systems.
• Experiment often, document clearly, and ship code that matters.

Technologies / techniques used:

• Python (heavy use)
• SQL (PostgreSQL, BigQuery)
• Graph libraries (e.g. NetworkX, cuGraph, Graphistry)
• Visualization tools (Plotly, Dash, D3, etc.)
• Neo4j or other graph databases
• GCP (preferred) or any major cloud provider
• GitHub + CI/CD pipelines

What you'll need:

• Strong Python and SQL skills.
• Ability to break down abstract problems into experimental pipelines.
• Clear communication skills in Portuguese and English.
• Curiosity and initiative. You find answers, you don’t wait for them.
• Solid data wrangling and visualization abilities.
• Willingness to go deep into Network Science, even if you’re not an expert (yet).

Nice to have:

• Experience with NetworkX, Neo4j, or any graph database.
• Previous Experience or openness to learn and work with Elixir codebases
• Background in graph theory, link prediction, community detection.
• Knowledge of probabilistic modeling or LLM integration with graph-based systems.
• Familiarity with GCP and large-scale data pipelines.

Recruiting process outline:

• Open-ended technical case – you’ll analyze a small graph dataset and extract insights.
• Technical interview – discuss your solution and background.
• Cultural interview – aligned expectations, mutual fit.
• If you’re not interested in doing a real technical assessment or engaging in an honest conversation, don’t apply.