樱花动漫

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Staff Data Scientist

Category Data Location San Francisco, California; San Diego, California; Tucson, Arizona; Mountain View, California Job ID 2025-70296

Company Overview

樱花动漫 is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.

Job Overview

We鈥檙e seeking a Staff Data Scientist to lead the development of a next-generation AI-native analytics platform that transforms how 樱花动漫 synthesizes insights from structured and unstructured data. This internal-facing platform is designed to power real-time decision-making across multiple business units by drastically accelerating time-to-insight, improving analytical consistency, and minimizing the manual burden of data synthesis.

This role calls for a deeply technical, strategic, and collaborative leader who thrives at the intersection of data science, backend engineering, and AI innovation. You will own the end-to-end design and implementation of this solution, which will ingest signals from a variety of data sources鈥攖ransaction logs, clickstream data, operational systems, Slack, JIRA, and more鈥攁nd convert them into digestible narratives, dashboards, and data products that power executive and operational decisions.

Responsibilities

Lead End-to-End Data Science Projects

Architect and build an enterprise-grade analytics system capable of querying, interpreting, and narrating insights from large volumes of fragmented data sources.

 

Lead development from initial scoping and experimentation to deployment of a production-grade, Kubernetes-native microservice.

 

Drive AI-Native Solutions

Design solutions using LLM architectures, retrieval-augmented generation (RAG), and multi-agent orchestration frameworks.

 

Translate business needs into intelligent agents that can autonomously identify KPIs, root causes, anomalies, and trends across datasets.

 

Shape Experimentation & Causal Inference

Lead the development of advanced experimentation systems including A/B/n tests, bandits, and painted-door experiments.

 

Apply and guide the use of causal inference techniques (e.g., Propensity Score Matching, Difference-in-Differences, Synthetic Controls) to measure outcomes and inform decision-making.

 

Influence Strategic Decision-Making

Serve as a thought leader to define analytical workflows that minimize time-to-insight and maximize actionability across business segments.

 

Influence leaders across product, operations, and data teams using clearly synthesized, high-impact insights.

Qualifications

Demonstrated ability to independently drive large-scale analytics or AI systems from conception to production.

 

Deep expertise in Python, SQL, and data tooling with ability to work across structured and semi/unstructured data.

 

Familiarity with microservices architecture, container orchestration (Kubernetes), and scalable infrastructure.

 

Advanced modeling skills in both traditional ML and Generative AI, particularly with open-source or custom LLMs.

 

Experience in web experimentation frameworks and statistical analysis methodologies.

 

Strong storytelling and executive influence capability through data.

 

Nice-to-Have:

Prior experience in cross-functional enterprise platforms or data products used by analysts, product managers, or operations teams.

 

Background in customer support analytics or diagnostic tooling across service platforms.

 

Experience deploying systems that interact with chat logs, documentation, or metrics across diverse internal tooling ecosystems.

 

Impact Scope

The analytics platform you build will:

 

Accelerate time-to-insight by over 95%, reducing multi-day data gathering and synthesis workflows to under 5 minutes.

 

Act as a force multiplier for analysts and data scientists, enabling scalable support across teams.

 

Support decision-making by automatically assembling narratives and dashboards from distributed data fragments—across BI tools, operational logs, conversational data, and mor

樱花动漫 provides a competitive compensation package with a strong pay for performance rewards approach. The expected base pay range for this position is Bay Area California $186,000-252,000, Southern California $179,000-242,000. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at . Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, 樱花动漫 conducts regular comparisons across categories of ethnicity and gender.

We use the technology for good to help small businesses and consumers.

Ercan Kaynakca Staff Data Crypto Analyst