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Tech Stack
Tools & technologiesAWSCloudNumpyPandasPySparkPythonScikit-LearnSparkSQLTableau
About the role
Key responsibilities & impact- Deploy data-driven exploratory analysis, predictive models, and AI solutions to solve business problems across financial services, particularly Risk, Fraud, Marketing, and Portfolio Management
- Convert machine learning lifecycle results into actionable product recommendations for internal and external presentation
- Lead the design, development, validation, deployment, and monitoring of advanced analytics, machine learning, and AI solutions
- Analyze and prepare data using data structures and algorithms, assembling datasets from standard and novel data sources
- Apply machine learning, predictive analytics, natural language processing, and emerging AI techniques to solve complex payments and financial services problems
- Design and execute experiments, hypothesis testing frameworks, and statistical analyses
- Establish and promote best practices in data science, machine learning, feature engineering, experimentation, model governance, and MLOps
- Communicate analytical findings through storytelling, executive-ready presentations, dashboards, visualizations, and self-service analytics tools
- Stay current on machine learning, AI, Generative AI, and financial services analytics trends and bring relevant innovations to the team
Requirements
What you’ll need- Master's degree or higher in Mathematics, Computer Science, Engineering, Operations Research, Statistics, or a related quantitative discipline
- 5+ years of experience developing and deploying end-to-end machine learning, predictive analytics, and data science solutions within the Payments, Banking, or Financial Services industry
- Strong proficiency in Python and SQL
- Experience with big data technologies such as Spark and PySpark is a plus
- Hands-on experience with data wrangling, feature engineering, and model development using Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, or Plotly
- Demonstrated experience building and deploying machine learning models in a production or near-production environment
- Proficiency with data visualization and business intelligence tools such as Tableau or equivalent
- Strong analytical thinking and problem-solving skills; ability to translate ambiguous business problems into rigorous analytical frameworks
- Ability to work collaboratively across product, engineering, and business teams
- Experience within the Payments, Banking, or Financial Services industry
- Hands-on experience with Databricks, including MLflow, Model Registry, collaborative notebooks, and MLOps workflows
- Experience deploying cloud-native machine learning solutions, particularly within AWS environments
- Working familiarity with Transformer Models and Agentic AI technologies
- Knowledge of model governance, regulatory compliance, and MLOps best practices within regulated financial services environments
- Current and future sponsorship are not available for this position
Benefits
Comp & perks- A voice in the future of fintech
- Always-on learning and development
- Collaborative work environment
- Opportunities to give back
- Competitive salary and benefits