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About This Role
Global Operations
Lead Data Scientist, Transparency & Observability - Trust and Safety
Location :
New York
Employment Type :
Regular
Job Code :
A107675
Responsibilities
The Transparency & Observability Data Science team builds the data, metrics, and analytical capabilities that help us understand how our enforcement systems perform. Our work supports both internal decision-making and external transparency reporting, while improving the reliability, explainability, and quality of Trust & Safety data. As a Lead Data Scientist on this team, you will be the technical architect for how Trust & Safety measures, monitors, and explains itself. You will design the systems and data foundations behind our metrics, observability, and transparency reporting, and serve as the AI and data architecture lead who drives AI readiness across Trust & Safety. Responsibilities: - Architect Transparency & Observability Systems: - Establish the Transparency & Observability architecture for Trust & Safety, covering metrics, data pipelines, monitoring, and reporting. - Design end-to-end observability for the enforcement lifecycle, from detection and review to actions and outcomes, so that every enforcement decision can be traced, measured, and explained. - Set standards for metric definitions, anomaly detection, and alerting, and make sure Trust & Safety reports one consistent view of its enforcement. - Build Governed, AI-Ready Data Foundations: - Set up data quality, lineage, and governance practices (SLAs, validation, reconciliation, documentation) so that Trust & Safety data is accurate, timely, and audit-ready for regulatory reporting. - Design and oversee ETL pipelines and workflow orchestration that turn scattered inputs into reliable, well-documented data products. - Build data systems designed for agent consumption across Trust & Safety, covering canonical data models, semantic layers, intelligent workflows, and evals. - Drive AI Readiness and Technical Leadership Across Trust & Safety: - Partner with Product, Engineering, Policy, Operations, Legal, and Comms to find high-impact opportunities where AI and data can improve operational efficiency and safety outcomes, and take solutions from experimentation to reliable production. - Own the long-term technical roadmap for Trust & Safety observability and AI readiness, and lead the program and change management needed to land it across teams. - Create reusable architecture principles, reference architectures, and best practices for responsible, explainable AI adoption across Trust & Safety teams, and mentor data scientists and engineers in applying them.
Qualifications
Minimum Qualification(s): - 5+ years of experience in Data Science, Data Engineering, Data Architecture, Analytics, or a related quantitative field, including technical leadership of cross-team initiatives. - A track record of designing scalable data ecosystems: data modeling, governed analytics layers, and production pipelines on large-scale platforms (e.g. Hive, Spark). - Strong SQL and Python skills. - Experience building data quality, monitoring, or observability solutions, and designing metrics frameworks used across organizations. - A solid understanding of AI architecture patterns (e.g. RAG, embeddings, knowledge systems) and responsible AI practices, applied to operational workflows. - Ability to translate ambiguous business problems into technical strategy, and to influence both technical and non-technical stakeholders, including leadership. - Bachelor's degree or above in a quantitative discipline such as Statistics, Computer Science, Mathematics, Engineering, or Economics. Preferred Qualification(s): - Experience in Trust & Safety, regulatory reporting, or compliance domains. - Familiarity with transparency and regulatory requirements such as the EU Digital Services Act (DSA) and similar global frameworks. - Experience driving program and change management across global, cross-functional teams.
Job Information
【For Pay Transparency】Compensation Description (Annually)
The base salary range for this position in the selected city is $219000 - $353600 annually.
Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.
Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).
The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
For Los Angeles County (unincorporated) Candidates:
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:
- Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
- Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
- Exercising sound judgment.
Trust & Safety
Content that this role interacts with includes images, video, and text related to every-day life, but it can also include (but is not limited to) bullying; hate speech; child safety; depictions of harm to self and others, and harm to animals. Hence, it is possible that this role will be exposed to harmful content on a daily basis.
TikTok recognises that keeping our platform safe for the TikTok communities is no ordinary job which can be both rewarding and psychologically demanding and emotionally taxing for some. This is why we are sharing the potential hazards, risks and implications in this unique line of work from the start, so our candidates are well informed before joining.
We are committed to the wellbeing of all our employees and promise to provide comprehensive and evidence-based programs, to promote and support physical and mental wellbeing throughout each employee's journey with us. We believe that wellbeing is a relationship and that everyone has a part to play, so we work in collaboration and consultation with our employees and across our functions in order to ensure a truly person-centred, innovative and integrated approach.
About TikTok
TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.
Why Join Us
Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.
We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.
Diversity & Inclusion
TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.
TikTok Accommodation
TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://tinyurl.com/RA-request
Salary Context
This $219K-$353K range is above the 75th percentile for Data Scientist roles in our dataset (median: $162K across 195 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 11,520 AI roles we're tracking, Data Scientist positions make up 4% of the market. At TikTok, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,564 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($286K) sits 51% above the category median. Disclosed range: $219K to $353K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
TikTok AI Hiring
TikTok has 55 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, Research Engineer, Research Scientist. Positions span San Jose, CA, San Jose, CA, US, New York, NY, US. Compensation range: $208K - $480K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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