Data Scientist

About the role

We are Payactiv, a FinTech company in the heart of Silicon Valley, devoted to giving workers access to their earned wages when they need them. Payactiv is the pioneer and industry leader in Earned Wage Access. We are the only Certified B Corporation and Public Benefit Corporation in our industry. Our app provides millions with financial services that help them avoid debt, manage their finances, and regain flexibility to pay for things on their own schedule. The Decision Analytics team is responsible for building the data-driven intelligence that powers many of Payactiv’s financial products and business decisions. We develop forecasting models, decision engines, risk strategies, marketing campaign analytics and other analytical solutions that help drive business performance at scale. Working closely with Product, Engineering, Operations and other Finance teams, we turn data into actionable insights and automated decisions that improve customer outcomes, strengthen partner relationships, and support long-term growth.

Responsibilities

• Own the end-to-end solution from data exploration and feature engineering through model training, validation, deployment, and performance monitoring.
• Develop and evaluate statistical and machine learning models, with clear justification for methodology and model selection.
• Write production-quality Python and SQL against multi-million-row datasets, with attention to performance, accuracy, and reliability.
• Translate ambiguous business questions into structured analyses and deliver actionable recommendations to stakeholders.
• Communicate analytical findings clearly to internal stakeholders, translating technical results into insights that support business decisions.
• Collaborate with data engineers in designing, validating, and maintaining ETL processes and data pipelines to ensure integrity, quality, and observability.
• Stay on top of emerging technologies in analytics, ML/AI, and cloud computing (familiarity with Snowflake and AWS SageMaker is a strong plus).

Requirements

• Bachelor’s or Master’s degree in a quantitative discipline – Data Science, Engineering, Statistics, Mathematics, Computer Science, Economics, etc.
• 1–3 years of experience in Data Science, Engineering or a closely related role.

Qualifications

• Proven proficiency in Python (pandas, NumPy, scikit-learn) and SQL, with experience in data transformation, wrangling, and analysis against multi-million-row datasets.
• Working knowledge of statistical modeling and machine learning fundamentals, including model selection trade-offs, data leakage, class imbalance, and validation methodology.
• Hands-on experience across a range of modeling approaches, which may include regression and generalized linear models, tree-based and ensemble methods, time series forecasting, clustering, or neural networks.
• Solid working knowledge of relational databases — comfortable writing performant queries, optimizing slow ones, and reasoning about schemas, joins, and query plans at production scale.
• Demonstrated ability to work independently and manage competing priorities with limited supervision.
• Excellent communication skills with the ability to convey complex findings to both technical and non-technical audiences.
• A proactive, curious, and detail-oriented mindset with strong problem-solving abilities.

Skills

• Hands-on experience with business intelligence and visualization tools (Sigma, Tableau, Looker, or Power BI).
• Experience designing, validating, and maintaining ETL processes and data pipelines.
• Familiarity with Snowflake and AWS SageMaker (a strong plus).

Benefits

• Company sponsored Health, Dental, and Vision insurance
• 401K, traditional, and Roth with a company match
• Tuition Assistance or Tuition Reimbursement
• Unlimited Paid Time off
• Monthly Gym Reimbursement
• Paid time off to volunteer
• Paid Family Leave
• Complimentary office lunches