Converge for FSI - Data Scientist, Signals for FSI_Products & Platforms
About The Position
The Data Scientist will join Deloitte’s Converge for FSI Signals for FSI team, part of the Innovation & Delivery Transformation team. The role builds and refines the analytical engine behind Signals for FSI and future products in the Converge for FSI portfolio, combining data science, product development, and financial services expertise. The work involves developing models and insights from financial services data to support products that achieve product-market fit and deliver actionable outcomes for clients. The Data Scientist will work hands-on with financial services data and collaborate with product, engineering, sales, and client-facing teams. Early use cases include retail banking, investment and wealth management, and insurance, with additional areas expected over time. The role offers the opportunity to work with start-up speed and Deloitte’s enterprise scale. Recruiting for this role ends on 12/1/2026. The position may be filled in one of 83 listed locations.
Requirements
- Bachelor’s degree in Computer Science, Statistics, Data Science, Mathematics, or a related quantitative field.
- At least 4 years of experience in applied data science, machine learning, or statistical modeling, including hands-on model development and validation.
- Proficiency in Python, SQL, and other data transformation and analytical tools.
- Ability to travel 10–25% on average, depending on the work and clients or industries served.
- Must be legally authorized to work in the United States without employer sponsorship now or in the future.
- Genuine interest in and working knowledge of the financial services industry, with curiosity about how data translates into client outcomes.
- Strong analytical rigor and sound judgment about when a signal or model is ready to ship or needs further refinement.
- Ability to move between hands-on technical work and clear communication with product, engineering, and business stakeholders.
- Self-directed and organized, with ability to manage multiple analytical workstreams.
- Collaborative approach and willingness to work closely with engineering and product teams.
- Comfort working through ambiguous, messy problems with creativity and persistence.
Nice To Haves
- Master’s degree in Computer Science, Statistics, Data Science, Mathematics, or a related field.
- At least 2 years of experience working with financial services data or delivering analytics solutions to financial services clients.
- At least 2 years of experience working with customer data to build audiences and segments for sales and marketing use cases.
- Experience with cloud-based data and analytics platforms such as Snowflake, Databricks, AWS, Google Cloud, or Azure in financial services or another highly regulated industry.
- Experience with MCP servers and using AI to analyze large datasets.
Responsibilities
- Develop, test, and refine statistical and machine learning models using financial services datasets such as consumer, business, transaction, account, risk, and market data.
- Partner with the Product team to translate financial services use cases into data requirements, features, data signals, and model specifications.
- Conduct exploratory data analysis and validate data quality across structured and semi-structured financial services data sources.
- Evaluate model performance against relevant business and regulatory considerations and iterate based on findings.
- Translate analytical findings into clear, business-relevant insights for Product, Sales, and client-facing teams.
- Stay current on emerging statistical and AI/ML techniques and financial services data trends, and evaluate their application to the Signals for FSI roadmap.
- Collaborate with engineering and product teams to bring models into production.
Benefits
- Broad range of employee benefits.
- May be eligible to participate in a discretionary annual incentive program, subject to program rules and applicable factors.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
