Data Scientist II - Machine Learning

The Opportunity :

This is an exciting opportunity for individuals with a solid foundation in data science to apply their expertise to real-world fintech challenges.

You will contribute to developing innovative solutions across various critical areas, directly impacting our product and business strategies.

What You'll Do :

As a Data Scientist II, you will be instrumental in turning data into actionable insights and robust solutions.
Your responsibilities will include :

- Solution Development : Assist in the development of data-driven solutions for key areas such as credit scoring, fraud detection, customer segmentation, and customer lifecycle modeling.

- Experimentation : Participate in the design and execution of A/B tests and other experiments to measure the impact of various product features and marketing initiatives.

- Model Building & Deployment : Contribute to the building, testing, and deployment of machine learning models for applications like underwriting, churn prediction, and pricing optimization.

- Data Analysis : Work with large, complex datasets from internal systems (e.g., payments, transactions, KYC) and external APIs (e.g., credit bureaus, open banking) to extract insights.

- Cross-functional Collaboration : Collaborate closely with product, engineering, risk, and growth teams to understand business problems, gather data requirements, and contribute to the development of models or dashboards.

- Communication of Insights : Clearly communicate findings and insights from data analysis and model performance to both technical and non-technical stakeholders through reports, dashboards, and presentations.

- Reporting & Monitoring Support : Support the definition and monitoring of key performance indicators (KPIs) and assist in creating automated reporting pipelines to enable data-driven decision-making.

What We're Looking For :

Required :

Experience : 2-4 years of hands-on experience in a data science or analytics role, preferably within the fintech, banking, or a high-growth technology environment.

Technical Proficiency :

- Proficient in SQL for data extraction, manipulation, and analysis.

- ML Fundamentals : Practical experience with machine learning models, ideally including exposure to models deployed in production environments.

- Experimentation Knowledge : Familiarity with experimentation frameworks, A/B testing methodologies, and basic concepts of causal inference.

- Communication : Ability to clearly and concisely communicate data-driven insights and technical concepts to both technical and non-technical audiences.

Preferred (Bonus Skills) :

- ML Ops Awareness : Familiarity with basic ML Ops tools, model monitoring techniques, and real-time analytics concepts.

- Regulatory Understanding : Awareness of regulatory environments relevant to financial services (e.g., FCRA, GDPR, PSD2).

- Tooling Exposure : Experience with tools like Airflow, DBT, Snowflake, Looker, or Tableau