Senior Data Scientist
About The Position
As a Senior Data Scientist, you will help shape the modeling, analytics, experimentation, and machine learning capabilities that support Merchant Opportunity Analysis (MOA) and Offer Engine within the Digital Onboarding team. Merchant Opportunity Analysis (MOA) refers to the analytical capability used to identify merchant needs, growth opportunities, product fit, and offer recommendations that can improve onboarding, personalization, and customer acquisition outcomes within Digital Onboarding. This role will focus on developing models and analytical approaches that improve onboarding experiences, customer acquisition, personalization, offer relevance, and measurable business outcomes. You will work hand in hand with Data & ML Engineers, backend engineers, product teams, analytics partners, and business stakeholders to turn customer, merchant, product, and application data into actionable insights and production-ready machine learning solutions.
Requirements
- 8+ years of experience in data science, machine learning, applied statistics, advanced analytics, or ML engineering.
- Strong hands-on experience with Python, SQL, pandas, scikit-learn, and common data science libraries.
- Experience building classification, clustering, recommendation, propensity, ranking, segmentation, or similarity-based models.
- Experience working with customer, merchant, application, product, transaction, or behavioral datasets.
- Strong understanding of feature engineering, model validation, experimentation, performance measurement, and model explainability.
- Experience using cloud-based data and ML platforms such as AWS SageMaker, Snowflake, S3, Glue, or comparable platforms.
- Ability to partner with engineering teams to productionize models and support MLOps practices.
- Strong analytical storytelling skills with the ability to explain model outcomes, trade-offs, and recommendations to non-technical stakeholders.
- Understanding of data quality, model drift, bias, fairness, monitoring, and responsible AI practices.
- Strong collaboration skills across product, engineering, analytics, marketing, and business teams.
- Bachelor’s degree in Computer Science, Information Technology, Information Systems, or a related field (or equivalent industry experience).
Nice To Haves
- Experience supporting offer engines, recommendation systems, personalization platforms, customer acquisition, or digital onboarding.
- Experience with nearest-neighbor matching, merchant segmentation, propensity modeling, uplift modeling, next-best-action, or look-alike modeling.
- Experience designing experiments, A/B tests, champion/challenger models, and business impact measurement frameworks.
- Experience with MLOps, model registries, feature stores, automated retraining, and model monitoring.
- Experience within financial services, fintech, payments, merchant services, or digital commerce.
- Experience with Generative AI, LLM-powered analytics, Agentic workflows, or AI-assisted model development.
- Advanced degree in data science, statistics, computer science, engineering, mathematics, economics, or a related field.
Responsibilities
- Develop machine learning models, scoring approaches, and analytical methods that support MOA, Offer Engine, customer insights, personalization, and onboarding optimization.
- Analyze customer, merchant, application, product, behavioral, and operational data to identify patterns and improvement opportunities.
- Build and refine models for segmentation, recommendation, propensity, similarity matching, ranking, personalization, and offer relevance.
- Partner with Data & ML Engineers to define feature requirements, validate feature quality, and transition models into production workflows.
- Design and evaluate experiments, A/B tests, champion/challenger approaches, and KPI measurement frameworks.
- Translate business objectives into data science solutions that improve customer acquisition, onboarding completion, engagement, and offer performance.
- Monitor model performance, drift, fairness, explainability, data quality, and business effectiveness in partnership with engineering teams.
- Create model documentation, explainability summaries, analytical narratives, and stakeholder-ready recommendations.
- Collaborate with Product, Analytics, Marketing, Engineering, and Business stakeholders to embed model outputs into Digital Onboarding experiences.
- Contribute to responsible AI practices, model governance, reproducibility, and enterprise ML standards.
Benefits
- standard background checks
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What This Job Offers
Job Type
Full-time
Career Level
Senior
