Director of Data Science

Biz2Credit Inc•New York, NY

just now

About The Position

At Biz2Credit, we are looking for individuals ready to join a dynamic and innovative fintech company on a mission to change the lending landscape for small businesses. Our values of Collaboration, Responsibility, Empowerment, Disruption, Innovation, and Trust guide everything we do, and our purpose of helping small businesses succeed drives us forward. We believe that with the right tools and support, small business owners can achieve their dreams, and we're here to make that happen. That's why we're dedicated to developing cutting-edge solutions, like our Biz2X platform, a fully configurable SaaS solution that leverages artificial intelligence and machine learning to make lending more efficient, effective, and accessible. We're a team of individuals who bring their unique personalities, backgrounds, and experiences to work every day. We believe that diversity makes us stronger, and that's why we value a culture that is inclusive and supportive. We're looking for people who are excited about the opportunity to make a difference, who want to work in an environment that is both challenging and fun, and who are eager to bring their whole selves to work. This is a rare opportunity to join a fast-growing team, where you will play a major role in shaping Biz2X's future. We're looking for an exceptional candidate who is excited about the opportunity to build a next-generation financial services business. We believe that there is a tremendous opportunity to leverage cutting-edge data science to inform smarter, faster decision-making. As a Biz2Credit leader in data science, you will shape the company's data-centric culture, work closely with our engineering team to develop our analytics infrastructure, and collaborate closely with our Chief Risk Officer on developing, validating, and automating our customer conversion and underwriting models. Hands-on credit risk/modelling experience in a consumer/small business lending financial institute is a must.

Requirements

Master's or Ph.D. degree in Computer Science, Statistics, Mathematics, Economics, or a related field.
8+ years of experience in data science, machine learning, or artificial intelligence roles, preferably in the Lending or financial services industry.
3+ years of experience in a leadership or management position, with a proven track record of successfully leading and developing high-performing teams.
Proficiency in programming languages such as Python, as well as in data manipulation and analysis tools (e.g., Pandas, NumPy, scikit-learn).
Strong understanding of statistical methods, machine learning algorithms, and data visualization techniques.
Expertise in NLP techniques and text analytics.
Familiarity with neural network architectures and deep learning frameworks (Pytorch)
Experience with big data technologies and platforms (e.g. AWS) is highly desirable.
Excellent communication skills, with the ability to translate complex technical concepts into business insights and recommendations.
Demonstrated ability to drive innovation, influence stakeholders, and deliver results in a fast-paced, dynamic environment.
Hands-on credit risk/modelling experience in a consumer/small business lending financial institute is a must.

Nice To Haves

Experience with big data technologies and platforms (e.g. AWS)

Responsibilities

Lead, mentor, and develop a team of data scientists, providing guidance and support in their professional growth and career advancement.
Foster a collaborative and inclusive team culture that encourages creativity, critical thinking, and continuous learning.
Define team goals, priorities, and performance metrics aligned with the company's strategic objectives.
Collaborate with product, marketing, and engineering teams to analyze and optimize the application funnel, leveraging data-driven insights to enhance user acquisition, conversion rates, and retention.
Utilize predictive modelling and machine learning techniques to identify factors influencing user behavior throughout the application process, enabling targeted interventions to improve user experience and maximize conversion rates.
Implement A/B testing methodologies and experimentation frameworks to iteratively refine and optimize the application funnel, driving continuous improvement in key performance metrics such as conversion rates, customer lifetime value, and user engagement.
Monitor and analyze key metrics related to the application funnel, such as click-through rates, bounce rates, drop-off points, and conversion funnels, to identify areas for improvement and develop data-driven strategies to address them.
Collaborate with cross-functional teams to implement data-driven solutions, such as personalized user experiences, dynamic content optimization, and predictive lead scoring, to streamline the application process and enhance user engagement at each stage of the funnel.
Stay informed about industry best practices, emerging trends, and innovative technologies related to application funnel optimization, and proactively recommend new approaches and strategies to drive continuous improvement and competitive advantage.
Lead the development and maintenance of credit risk models, leveraging advanced statistical techniques and machine learning algorithms to assess and mitigate credit risk across various financial products and customer segments.
Collaborate with risk management and credit underwriting teams to define model requirements, gather relevant data sources, and validate model performance against established benchmarks and regulatory requirements.
Incorporate both traditional and alternative data sources, such as transactional data, behavioral data, and alternative credit data, to enhance the predictive power and robustness of credit risk models.
Monitor the performance of credit models over time, conducting regular model reviews, back testing, and sensitivity analyses to ensure accuracy, stability, and alignment with business objectives.
Work closely with cross-functional teams to implement credit models into decision-making processes, such as loan origination, credit scoring, and portfolio management, and provide ongoing support and guidance to ensure effective model deployment and utilization.
Stay abreast of regulatory developments, industry trends, and best practices related to credit modelling and risk management, and proactively recommend enhancements and refinements to existing models to adapt to changing business environments and regulatory requirements.
Collaborate with cross-functional teams to define and implement data strategies that support business goals and drive innovation in the lending domain.
Establish and enforce data governance policies and best practices to ensure data quality, integrity, and compliance with regulatory requirements.
Lead the design, development, and deployment of advanced analytics models and machine learning algorithms to extract actionable insights from large, complex datasets.
Apply statistical techniques and predictive modelling to identify trends, patterns, and correlations that drive business value and enhance customer experiences.
Partner with product managers and engineers to integrate data science solutions into existing products and platforms, as well as to conceptualize and develop new data-driven products and features.
Continuously monitor and optimize the performance of data science algorithms and models to ensure scalability, efficiency, and accuracy.
Collaborate closely with key stakeholders, including executives, business leaders, and external partners, to understand business requirements, communicate insights, and influence decision-making.
Present findings, recommendations, and project updates clearly and compellingly through visualizations, reports, and presentations.