• Act as a playing coach in product and marketing analytics; • Identify root causes of changes in product KPIs; • Lead user behavior analysis and funnel analysis; • Lead design, evaluation, and interpretation of experiments; • Partner closely with Product, Marketing, and Growth teams; • Lead, mentor, and develop a team of product analysts; • Build and scale a systematic marketing analytics framework; • Serve as the main analytics partner for stakeholders.

• Tell clear, business-relevant stories about our talent using data and insights. • Get hands-on with AI to explore where it meaningfully improves insight generation, decision support, and experimentation. • Design, own, and evolve the People data foundation, including pipelines and BigQuery data models, in partnership with Data Engineering and HR Operations. • Build scalable analytics products and tools, including Tableau dashboards and AI-enabled products. • Partner cross-functionally with People Team and business leaders to identify and scope high-impact talent questions, analyze using quantitative methods, and translate insights into clear, actionable recommendations. • Own initiatives end-to-end across the People Team (Recruiting, Talent Management, DEIB, and more). • Foster a culture of data-informed decision-making across the People Team and the broader organization.

• Lead the strategic direction of data science for the next phase of Mission Lane’s growth • Innovate on the application of machine learning to underwriting and other practical problems • Report directly to the COO • Lead one of the most knowledgeable and effective data science teams in fintech • Drive financial and customer outcomes through machine learning and cross-functional collaboration with the entire business • Develop the data science team to add new data sources, improve modeling methodology, and practice sound risk management

• Responsible for refactoring the Optimization algorithm written in Python using Object Oriented Programming • Work on the latest applications of data science to solve business problems in the Supply chain and optimization space of Retail and/or CPG. • Utilize advanced statistical techniques and data science algorithms to analyze large datasets and derive actionable insights related to replenishment optimization and inventory allocation. • Develop and implement predictive models and optimization algorithms to improve inventory management, reduce stockouts, and optimize resource allocation across the supply chain. • Collaborate with cross-functional teams to understand business requirements and translate them into data-driven solutions. • Design and execute experiments to evaluate the effectiveness of different replenishment strategies and allocation policies. • Monitor and analyze key performance indicators (KPIs) related to replenishment and supply chain allocation, and provide recommendations for continuous improvement. • Stay abreast of industry trends and best practices in data science, replenishment optimization, and supply chain management, and leverage this knowledge to drive innovation within the organization. • Collaborate, coach, and learn with a growing team of experienced Data Scientists.