Principal Data Scientist

We’re looking for a Principal Data Scientist to help build and scale machine learning solutions that have real business impact. This is a hands-on role where you’ll own the full lifecycle of your work-from identifying problems to deploying models in production and continuously improving them. The environment is fast-moving and highly collaborative, with a strong focus on applying modern AI and ML techniques, including emerging work with large language models (LLMs).

What You’ll Be Doing

• Own end-to-end machine learning projects:
• Define problems
• Explore and prepare data
• Build and validate models
• Deploy to production and monitor performance
• Develop solutions across areas like:
• Customer behavior and personalization
• Anomaly detection and fraud
• Risk modeling and pattern recognition
• Build and improve production ML pipelines and systems
• Work with large-scale data platforms and complex datasets
• Experiment with AI/LLM tools and apply them to real-world problems
• Partner with product, engineering, and business teams to drive impact
• Translate technical work into clear insights and recommendations

• Strong experience in data science and machine learning (7+ years)
• Proven track record of deploying models into production
• Strong coding skills (Python + SQL)
• Experience working with large-scale data platforms (e.g., Spark, Databricks, Snowflake, or similar)
• Solid understanding of ML techniques (including time series, anomaly detection, NLP, etc.)
• Comfortable working in ambiguous environments without defined tasks
• Someone who proactively identifies problems and builds solutions
• Interest in and willingness to experiment with AI tools (LLMs, GenAI)

Nice to Have

• Experience with MLOps tools (MLflow, SageMaker, etc.)
• Experience with streaming or real-time systems
• Background in financial services, fintech, or similar domains
• Exposure to LLMs, RAG, or modern AI workflows

What Makes This Role Unique

• High level of ownership-you’re not working off a backlog of tickets
• Opportunity to build production systems, not just experiments
• Ability to shape what gets built and why
• Growing investment in AI and modern ML approaches
• Strong cross-functional exposure