Data Scientist II

Russell Tobin is a Fair Chance employer. We consider all qualified applicants, including those with criminal histories, in a manner consistent with applicable state and local Fair Chance laws and ordinances, including, the California Fair Chance Act and all applicable local Fair Chance ordinances. Accommodations We are committed to providing reasonable accommodations to applicants and employees with disabilities. If you require a reasonable accommodation to participate in the application or interview process, or to perform the essential functions of this role, please contact us. Only applicable for San Francisco Candidates: Under the San Francisco Lactation in the Workplace Ordinance, we will provide written notice of lactation accommodation rights, and this notice will automatically be given upon hiring, any inquiry of parental leave or lactation accommodation.

• BS or MS degree in Statistics, Mathematics, Computer Science, or a related field.
• 5+ years of experience in data science or product analytics (preferably in fintech), with a strong foundation in predictive modeling, customer segmentation, and experimentation.
• Ability to formulate data-backed strategies that will drive step-function growth for the business and increase customer benefit.
• Ability to generate hypotheses grounded in customer behavior, industry trends, and external market factors.
• Experience in designing and interpreting complex experiments beyond traditional A/B testing methods.
• Demonstrated experience in building reusable and scalable analytics solutions, with a focus on efficiency and avoiding duplication of work.
• Outstanding communication skills with the ability to influence decisionmakers and build consensus with teams.
• Quick learner, adaptable, with the ability to work independently or as part of a team in a fast-paced environment., * Advanced SQL skills and proficiency in visualization tools such as Qlik, Quicksight, Tableau, Plotly Dash.
• Strong analytical and modeling skills using Python (for its rich suite of statistical and modeling libraries like NumPy, Pandas, Scikit-learn, etc.).
• Experience with Causal inference techniques such as propensity score matching, difference-in-differences, and synthetic control methods.
• Experience with Generative AI and other evolving technologies to accelerate insights from multi-modal data.

Education Required Bachelor’s degree in an IT-related or Engineering field.

Education Preferred Bachelor’s degree in an IT-related or Engineering field.