Foster City, CA

Data Jobs in Foster City, CA

2 jobs found

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1mo ago

Data Scientist, Trust & Safety

Replit Foster City, CA

Foster City, CA

1mo ago Closed

$210k - $310k/yr Full time Data Analysis Data Analytics Data Integration

This position is no longer accepting applications (closed Sep 1, 2026).

Job Description

Seeking a data scientist to design and scale a Trust & Safety and anti-abuse program from the ground up, onsite in Foster City, CA.

Responsibilities

Own the analytical foundation for Trust & Safety, including abuse prevalence, fraud loss, false-positive/false-negative rates, time to detect, time to mitigate, appeal and reversal rates, and verification step-up conversion.
Build reliable datasets and dbt models linking product events, identity signals, payment activity, infrastructure usage, content classifications, enforcement actions, appeals, and support outcomes.
Develop and evaluate risk models, rules, and anomaly-detection systems for threats such as phishing, scam hosting, cryptomining, token farming, payment fraud, promotional abuse, and AI-agent exploitation.
Design rigorous offline evaluations, shadow-mode tests, holdouts, and controlled experiments to measure detection quality and user impact of new policies and enforcement actions.
Define thresholds and decision frameworks balancing abuse reduction, economic loss, customer friction, and false positives across free, paid, and enterprise users.
Investigate emerging abuse patterns, quantify impact, identify coordinated behavior, and translate signals into clear recommendations for product and engineering teams.
Develop predictive risk signals for accounts, devices, transactions, workspaces, or deployments and embed them into detection, review, and escalation workflows.
Partner with Support and Legal to improve case review, appeals, reason-code quality, and feedback loops so human decisions inform model and policy signals.
Build monitoring for drift and abuse adaptation, data-quality issues, and unintended harm to legitimate users.
Communicate findings clearly to technical and non-technical partners, outlining tradeoffs, uncertainty, and evidence behind high-impact decisions.

Requirements

5+ years of experience in data science, product analytics, fraud, risk, trust and safety, or related fields.
Strong SQL and Python skills with experience handling large behavioral datasets and building reliable data models or pipelines.
Experience developing and evaluating predictive models, experiments, or decision systems, with sound judgment around uncertainty and tradeoffs.
Ability to turn ambiguous data into clear recommendations and communicate effectively across technical and non-technical teams.
Comfort with imperfect labels, biased samples, and high-impact decisions where false positives matter.
Proficiency using AI tools to increase effectiveness while upholding analytical quality.

Technologies

dbt, BigQuery, Snowflake, Fivetran, Amplitude, Mixpanel, Segment, Python, SQL

Benefits

Competitive Salary & Equity
401(k) Program with a 4% match (US Only)
Health, Dental, Vision and Life Insurance
Short Term and Long Term Disability
Paid Parental, Medical, Caregiver Leave
Flexible Time Off (FTO) + Holidays
Commuter Benefits (In-Office Only)
Monthly Wellness Stipend
Autonomous Work Environment
In Office Set-Up Reimbursement (In-Office Only)
Quarterly Team Gatherings
In Office Amenities (In-Office Only)

Who You Are

You're a data scientist who moves fast, goes deep, and thinks adversarially, able to spin up analyses in hours rather than days without compromising rigor.
You investigate beyond top-line abuse metrics to understand selection effects, missing labels, policy changes, attacker adaptation, and hidden false positives.
You recognize that Trust & Safety data is imperfect and outcomes are high-stakes, with delayed or biased ground truth and evolving attacker behaviors; you stress-test work and distinguish correlation from the evidence needed for enforcement.
You leverage AI tools to amplify output—writing code, exploring data, generating hypotheses, and prototyping investigations—yet treat AI-assisted results as drafts and uphold a high standard for deliverables.

Examples of What You Could Do

Create a measurement framework for abuse exposure, reconcile incomplete labels across automated detections, human reviews, appeals, chargebacks, and support cases, and establish a trustworthy baseline.
Design a risk-scoring model for suspicious account clusters using identity, device, payment, graph, and product-behavior signals; set thresholds to reduce fraud while protecting legitimate users.
Review a phishing rule that appears precise but disproportionately flags paying users with legitimate brand references; redesign its evaluation and review path to lower false positives.
Quantify a progressive verification ladder, deciding when to escalate verification and weighing abuse prevention against legitimate-user conversion.
Detect coordinated token-farming or promotional abuse networks by merging account-linkage graphs, referral data, payment patterns, and infrastructure usage; collaborate with Engineering to operationalize findings.
Evaluate a new enforcement policy in shadow mode, estimate counterfactual impact, and advise launch, revision, or rejection prior to user exposure.

Preferred Qualifications

Experience building or evaluating anti-abuse, fraud, identity, security, spam, integrity, or content-safety systems at scale.
Developed, shipped, and maintained ML models in production (classification, anomaly detection, or risk scoring), including feature engineering on behavioral/transaction data, thresholding, and post-launch monitoring.
Experience with graph analysis, entity resolution, coordinated-behavior detection, reputation systems, anomaly detection, or risk scoring.
Experience measuring false positives and enforcement harm, designing human-review workflows, or using appeals and case outcomes as model feedback.
Familiarity with progressive verification, KYC, account trust, or identity providers such as Prove, Persona, Socure, or Stripe Identity.
Experience with causal inference methods such as difference-in-differences, propensity score methods, synthetic control, or uplift modeling.
Experience with a modern data stack such as dbt, BigQuery, Snowflake, Fivetran, Amplitude, Mixpanel, or Segment.
Experience at a consumer platform, developer tool, cloud provider, marketplace, fintech, or other product with meaningful adversarial exposure.

Bonus Points

Built AI-powered analytical tools, investigation systems, automated detections, or novel measurement approaches.
Experience with AI-native abuse such as prompt injection, LLM token farming, model extraction, or agent-driven abuse.
Understanding of freemium, usage-based, or promotional pricing and the abuse incentives they create.
Worked directly with operational review teams and can translate analytical signals into practical playbooks, queues, and escalation paths.

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1mo ago

Lead Analytics Engineer – Enterprise, Data & AI

Zoox Foster City, CA

Foster City, CA

1mo ago Closed

$236k - $284k/yr Full time Analytics Anaplan Apache Airflow

This position is no longer accepting applications (closed Aug 15, 2026).

Job Description

Zoox invites you to join as a Lead Analytics Engineer focused on Enterprise, Data & AI in Foster City, CA. This onsite role centers on designing and implementing a unified semantic layer and data modeling strategy to transform SAP, Workday, Salesforce, and Anaplan data into AI ready datasets. You will empower intelligent agents and enterprise analytics across the organization. The base salary ranges USD 236,000 to 284,000 per year, with equity components and a comprehensive benefits program.

Benefits

Paid time off including sick leave, vacation, and bereavement
Unpaid time off
Zoox Stock Appreciation Rights
Amazon RSUs
Health insurance
Long-term care insurance
Long-term and short-term disability insurance
Life insurance

Responsibilities

Design and sustain a unified semantic model that serves as the single source of truth for cross-functional stakeholders, AI agents, self serve analytics, and executive dashboards.
Collaborate with Data and AI Engineers to structure and optimize data for precise, low latency enterprise knowledge queries.
Establish standards for data modeling, version control, testing, and documentation to ensure high data quality and maintainable systems.
Develop automated testing and observability frameworks that detect data anomalies and enable self-healing pipelines, keeping data reliable for downstream consumption.
Partner with cross-functional business leaders to translate operational requirements into scalable, high-impact data solutions.

Requirements

10+ years in Data Engineering and Analytics, with hands-on experience building a semantic framework using Python, SQL and modern orchestration frameworks (Airflow, Lakeflow, Argo); at least 2+ years deploying AI generated code.
Extensive experience with modern data stacks (Snowflake, Databricks, BigQuery) to build complex, enterprise-grade data models.
Deep understanding of data structures within large-scale enterprise platforms (SAP S/4HANA, Salesforce, Workday, etc.) and the ability to reconcile disparate schemas into clean models.
Proven ability to design modular, scalable, and performant data architectures that are easy to use for downstream AI agents and analytics tools.
A track record of delivering technical projects from design to completion, mentoring junior engineers, and fostering a culture of collaboration and data excellence.

Technologies

Python
SQL
Airflow
Lakeflow
Argo
Snowflake
Databricks
BigQuery
SAP S/4HANA
Ariba
BRIM
ME
Workday
Salesforce
Anaplan
Tableau
Streamlit

Compensation overview

Base salary: USD 236,000 to 284,000 per year
Equity: Amazon RSUs and Zoox Stock Appreciation Rights
Sign-on bonus may be offered
Compensation varies by geographic location and level, with leveling influenced by years of experience, domain knowledge, and interview performance

Location detail: Foster City, CA, onsite.

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Data Jobs in Foster City, CA

Data Scientist, Trust & Safety

Lead Analytics Engineer – Enterprise, Data & AI

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