We are looking for two highly experienced and influential Senior Data Scientists (Data Scientist III) to join our rapidly expanding data science team.

This is a critical role for individuals with a proven track record of designing, building, and deploying impactful data-driven solutions in a dynamic fintech environment.

You will lead complex projects, mentor junior team members, and significantly contribute to our strategic data initiatives.

What You'll Do :

As a Senior Data Scientist, you will lead the charge in leveraging data to solve complex business problems.

Your responsibilities will include :

- Lead the design and development of advanced data-driven solutions across critical business areas such as credit scoring, sophisticated fraud detection systems, in-depth customer segmentation, and complex lifecycle modeling.

- Independently design, execute, and analyze sophisticated A/B tests and other experimental frameworks to rigorously measure the causal impact of product and marketing initiatives, providing strategic recommendations.

- Drive the end-to-end process of building, deploying, and maintaining high-performance machine learning models in production environments (e.g., for robust underwriting, proactive churn prediction, dynamic pricing optimization).

- Work extensively with very large and diverse datasets from complex internal systems (e.g., intricate payments networks, high-volume transactions, comprehensive KYC data) and integrate with external APIs (e.g., advanced credit bureaus, open banking platforms).

- Proactively collaborate with product, engineering, risk, and growth teams to deeply understand business challenges, define analytical approaches, gather nuanced requirements, and architect scalable data solutions or dashboards.

- Translate complex analytical findings and model insights into clear, concise, and compelling narratives for senior leadership and diverse non-technical stakeholders through sophisticated dashboards, comprehensive reports, and impactful presentations.

- Define and rigorously monitor key performance indicators (KPIs) at a strategic level, and design and implement robust, automated reporting and monitoring pipelines to support scalable, data-driven decision-making across the organization.

What We're Looking For :

Required :

Education : Bachelors or Masters degree in Data Science, Statistics, Computer Science, Mathematics, or a closely related quantitative field.

Experience :

- 4-8 years of extensive, hands-on experience in a data science or advanced analytics role, with a significant portion in fintech, banking, or a high-growth technology company.

- Deep expertise and demonstrated proficiency in Python, including advanced usage of libraries such as Pandas, NumPy, Scikit-learn, alongside experience with other relevant ML frameworks (e.g., TensorFlow, PyTorch).

- Expert-level proficiency in SQL for complex data querying, analysis, and pipeline development.

- Proven track record of successfully building, deploying, and maintaining complex machine learning models in production environments at scale.

- Strong practical experience in designing and analyzing complex experimentation frameworks, including advanced A/B testing methodologies and robust causal inference techniques.

- Exceptional ability to clearly, concisely, and persuasively communicate complex data-driven insights and technical concepts to both highly technical and senior non-technical audiences, influencing business decisions.

Preferred (Highly Valued Skills) :

- Significant experience and expertise in specific fintech domains such as credit risk modeling, advanced fraud detection, or sophisticated financial forecasting.

- Hands-on experience with ML Ops tools and best practices, model monitoring, continuous integration/continuous deployment (CI/CD) for ML, and real-time analytics architectures.

- In-depth understanding and experience navigating relevant regulatory environments within financial services (e.g., FCRA, GDPR, PSD2, etc.).

- Proficiency with data orchestration tools (e.g., Airflow), data transformation tools (e.g., DBT), modern data warehouses (e.g., Snowflake), and advanced business intelligence platforms (e.g., Looker, Tableau).

- Experience mentoring junior data scientists or leading small project teams