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トレンド企業

トレンド企業

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求人TikTok

Measurement Partner for Performance CoE- Brazil

TikTok

Measurement Partner for Performance CoE- Brazil

TikTok

São Paulo, Brazil

·

On-site

·

Full-time

·

2mo ago

福利厚生

Equity

Parental Leave

Healthcare

必須スキル

TypeScript

JavaScript

PostgreSQL

Responsibilities

Team Introduction

The Marketing Science Measurement team is responsible for interpreting the performance of advertising on our platform. We work with advertisers to optimise their activity on the platform and build meaningful connections that help drive behaviour change.

Within the Marketing Science Team, we have a Centre of Excellence (CoE) that supports how the team sets standards for how we measure, learn, and scale performance across regions. We partner with regional teams and strategic advertisers to design robust measurement programs, build tooling, and translate evidence into decisions that improve business outcomes worldwide.

About the Role

The Measurement Partner will partner with our leading clients and agencies to help measure the effectiveness of their investment in our platform. In addition, this role will partner with key cross-functional teams such as sales, product marketing, measurement product and business marketing to ensure that clients and agencies have best-in-class support.

The role within the CoE is a hands-on, highly collaborative role that blends strategy, statistical rigor, and enablement. You will co-own global learning agendas, design and operationalize experimentation at scale, and develop playbooks and frameworks (e.g., causal experiments, ad-stock & saturation, cross-channel attribution hygiene) that raise the bar for how our regions plan, test, and optimize. You will also coach practitioners, review code and methods, and turn proof-of-concepts into repeatable programs.

Global Strategy & Governance

  • Co-design global learning agendas and experimentation roadmaps with Sales, Product Marketing, and regional Marketing Science teams.
  • Establish standards for design, analysis, documentation, and decisioning (e.g., hypothesis templates, guardrails, code review, reproducibility).
  • Translate complex methods into clear recommendations for executives and practitioners.

Experimentation & Causal Inference at Scale

  • Architect and QA A/B, geo-lift, incrementality, and calibration studies; select appropriate designs under privacy and data constraints.
  • Build scalable templates that regional teams can reuse with minimal modification.
  • Run post-test evidence reviews to codify learnings and update learnings.

Performance Science Frameworks

  • Develop and maintain approaches for ad-stock/decay, saturation & elasticity, budget-reallocation simulators, and incremental ROAS estimation.
  • Diagnose interactions across levers (e.g., brand → performance spillovers) and communicate trade-offs in plain language.

Code, Methods, and Enablement

  • Conduct method and code reviews (statistical assumptions, diagnostics, data QA, reproducibility).
  • Create and deliver training (live sessions, office hours, self-serve modules) on different statistical concepts.
  • Write reference implementations and concise documentation that others can maintain.

Cross-Functional Influence

  • Partner with Product/Measurement Product to provide field feedback and shape roadmaps; pilot new features with lighthouse clients.
  • Work with regional leaders to adapt global frameworks to local market structures and constraints; scale Po Cs into programs.

Client & Stakeholder Impact

  • Join senior client conversations as a trusted advisor, guiding how to test, measure, and act.
  • Convert insights into clear actions (budget moves, creative/media levers, sequencing) and measure the business impact of those actions.
  • Manage client and agency feedback to ensure their voice is reflected with our product and partnerships teams

Qualifications

Minimum Qualifications

  • At least 3-5 years of experience with digital advertising, large marketing-related organizations, or measurement suppliers.
  • Expertise in advertising data, measurement methods and statistical model; Demonstrated expertise in measurement design (A/B, geo/market tests, lift studies, calibration) and statistical modeling (ad-stock/decay, saturation, elasticity).
  • Basic knowledge in R or Python and SQL; comfort reviewing code and enforcing reproducibility standards (versioning, environments, documentation).
  • Successful examples where past measurement contributions led to behavior change and experience influencing senior stakeholders.
  • A track record of inferential problem solving through experimental design and analytical methods
  • Proficient in English (written and verbal) across technical and non-technical audiences due to supporting Global teams/clients
  • Strong prioritization and program-building mindset: start with PoC, scale to playbook.

Preferred Qualifications

  • Relevant bachelor's degree in Business, Advertising, Economics, Computer Science, Statistics or Research.
  • Familiarity with privacy-first innovation for measurement systems is a plus
  • Experience working with sales and cross-functional teams
  • Experience with advertising agencies or advanced marketing data-driven organizations preferred
  • Familiarity with MMM/advanced attribution (as inputs to experimentation, not as a replacement)
  • Experience working across multiple regions/time zones and with agencies or large advertisers.

総閲覧数

1

応募クリック数

0

模擬応募者数

0

スクラップ

0

TikTokについて

TikTok

TikTok

Late Stage

A short-form video entertainment app and social network platform

10,001+

従業員数

Los Angeles

本社所在地

$220B

企業価値

レビュー

3.8

10件のレビュー

ワークライフバランス

2.8

報酬

3.7

企業文化

4.1

キャリア

3.2

経営陣

2.9

68%

友人に勧める

良い点

Great team dynamics and support

Innovative and creative culture

Good learning opportunities

改善点

Work-life balance challenges

Fast-paced and stressful environment

High expectations and tight deadlines

給与レンジ

49件のデータ

Junior/L3

Junior/L3 · Anti-Fraud Data Analyst

3件のレポート

$143,750

年収総額

基本給

$125,000

ストック

-

ボーナス

-

$126,500

$163,300

面接体験

2件の面接

難易度

4.0

/ 5

期間

21-35週間

体験

ポジティブ 0%

普通 0%

ネガティブ 100%

面接プロセス

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Behavioral Interview

5

Final Round

6

Offer

よくある質問

Coding/Algorithm

Behavioral/STAR

Technical Knowledge

Culture Fit